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Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder | https://papers.nips.cc/paper_files/paper/2023/hash/0001ca33ba34ce0351e4612b744b3936-Abstract-Conference.html | Michael Bereket, Theofanis Karaletsos | https://papers.nips.cc/paper_files/paper/2023/hash/0001ca33ba34ce0351e4612b744b3936-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20165-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0001ca33ba34ce0351e4612b744b3936-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0001ca33ba34ce0351e4612b744b3936-Supplemental-Conference.pdf | Generative models of observations under interventions have been a vibrant topic of interest across machine learning and the sciences in recent years. For example, in drug discovery, there is a need to model the effects of diverse interventions on cells in order to characterize unknown biological mechanisms of action. W... | null |
Cross-Episodic Curriculum for Transformer Agents | https://papers.nips.cc/paper_files/paper/2023/hash/001608167bb652337af5df0129aeaabd-Abstract-Conference.html | Lucy Xiaoyang Shi, Yunfan Jiang, Jake Grigsby, Linxi Fan, Yuke Zhu | https://papers.nips.cc/paper_files/paper/2023/hash/001608167bb652337af5df0129aeaabd-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22418-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/001608167bb652337af5df0129aeaabd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/001608167bb652337af5df0129aeaabd-Supplemental-Conference.pdf | We present a new algorithm, Cross-Episodic Curriculum (CEC), to boost the learning efficiency and generalization of Transformer agents. Central to CEC is the placement of cross-episodic experiences into a Transformer’s context, which forms the basis of a curriculum. By sequentially structuring online learning trials an... | null |
PaintSeg: Painting Pixels for Training-free Segmentation | https://papers.nips.cc/paper_files/paper/2023/hash/0021c2cb1b9b6a71ac478ea52a93b25a-Abstract-Conference.html | Xiang Li, Chung-Ching Lin, Yinpeng Chen, Zicheng Liu, Jinglu Wang, Rita Singh, Bhiksha Raj | https://papers.nips.cc/paper_files/paper/2023/hash/0021c2cb1b9b6a71ac478ea52a93b25a-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19673-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0021c2cb1b9b6a71ac478ea52a93b25a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0021c2cb1b9b6a71ac478ea52a93b25a-Supplemental-Conference.zip | The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training. We propose an adversarial masked contrastive painting (AMCP) process, which creates a contrast between the original image and a painted image in which a masked area is painted using off-the-shelf generative models. Dur... | null |
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training | https://papers.nips.cc/paper_files/paper/2023/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html | Yiren Jian, Chongyang Gao, Soroush Vosoughi | https://papers.nips.cc/paper_files/paper/2023/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20571-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/002262941c9edfd472a79298b2ac5e17-Paper-Conference.pdf | null | We present a novel methodology aimed at optimizing the application of frozen large language models (LLMs) for resource-intensive vision-language (VL) pre-training. The current paradigm uses visual features as prompts to guide language models, with a focus on determining the most relevant visual features for correspondi... | null |
Path following algorithms for $\ell_2$-regularized $M$-estimation with approximation guarantee | https://papers.nips.cc/paper_files/paper/2023/hash/00296c0e10cd24d415c2db63ea2a2c68-Abstract-Conference.html | Yunzhang Zhu, Renxiong Liu | https://papers.nips.cc/paper_files/paper/2023/hash/00296c0e10cd24d415c2db63ea2a2c68-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22675-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/00296c0e10cd24d415c2db63ea2a2c68-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/00296c0e10cd24d415c2db63ea2a2c68-Supplemental-Conference.pdf | Many modern machine learning algorithms are formulated as regularized M-estimation problems, in which a regularization (tuning) parameter controls a trade-off between model fit to the training data and model complexity. To select the ``best'' tuning parameter value that achieves a good trade-off, an approximated soluti... | null |
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation | https://papers.nips.cc/paper_files/paper/2023/hash/0073cc73e1873b35345209b50a3dab66-Abstract-Conference.html | Yuhan Ding, Fukun Yin, Jiayuan Fan, Hui Li, Xin Chen, Wen Liu, Chongshan Lu, Gang Yu, Tao Chen | https://papers.nips.cc/paper_files/paper/2023/hash/0073cc73e1873b35345209b50a3dab66-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22964-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0073cc73e1873b35345209b50a3dab66-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0073cc73e1873b35345209b50a3dab66-Supplemental-Conference.pdf | Recent advances in implicit neural representations have achieved impressive results by sampling and fusing individual points along sampling rays in the sampling space. However, due to the explosively growing sampling space, finely representing and synthesizing detailed textures remains a challenge for unbounded large-s... | null |
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation | https://papers.nips.cc/paper_files/paper/2023/hash/007f4927e60699392425f267d43f0940-Abstract-Conference.html | Ruida Zhou, Tao Liu, Min Cheng, Dileep Kalathil, P. R. Kumar, Chao Tian | https://papers.nips.cc/paper_files/paper/2023/hash/007f4927e60699392425f267d43f0940-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19552-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/007f4927e60699392425f267d43f0940-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/007f4927e60699392425f267d43f0940-Supplemental-Conference.zip | We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testing environment. Previous policy-based robust RL algorithms mainly focus on the tabular setting under uncertainty sets that facilitate robust... | null |
Adaptive Selective Sampling for Online Prediction with Experts | https://papers.nips.cc/paper_files/paper/2023/hash/00b67df24009747e8bbed4c2c6f9c825-Abstract-Conference.html | Rui Castro, Fredrik Hellström, Tim van Erven | https://papers.nips.cc/paper_files/paper/2023/hash/00b67df24009747e8bbed4c2c6f9c825-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19791-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/00b67df24009747e8bbed4c2c6f9c825-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/00b67df24009747e8bbed4c2c6f9c825-Supplemental-Conference.zip | We consider online prediction of a binary sequence with expert advice. For this setting, we devise label-efficient forecasting algorithms, which use a selective sampling scheme that enables collecting much fewer labels than standard procedures. For the general case without a perfect expert, we prove best-of-both-worlds... | null |
Attentive Transfer Entropy to Exploit Transient Emergence of Coupling Effect | https://papers.nips.cc/paper_files/paper/2023/hash/00bb4e415ef117f2dee2fc3b778d806d-Abstract-Conference.html | Xiaolei Ru, XINYA ZHANG, Zijia Liu, Jack Murdoch Moore, Gang Yan | https://papers.nips.cc/paper_files/paper/2023/hash/00bb4e415ef117f2dee2fc3b778d806d-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21975-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/00bb4e415ef117f2dee2fc3b778d806d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/00bb4e415ef117f2dee2fc3b778d806d-Supplemental-Conference.zip | We consider the problem of reconstructing coupled networks (e.g., biological neural networks) connecting large numbers of variables (e.g.,nerve cells), of which state evolution is governed by dissipative dynamics consisting of strong self-drive (dominants the evolution) and weak coupling-drive. The core difficulty is s... | null |
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More | https://papers.nips.cc/paper_files/paper/2023/hash/00db17c36b5435195760520efa96d99c-Abstract-Conference.html | Jan Schuchardt, Yan Scholten, Stephan Günnemann | https://papers.nips.cc/paper_files/paper/2023/hash/00db17c36b5435195760520efa96d99c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21081-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/00db17c36b5435195760520efa96d99c-Paper-Conference.pdf | null | A machine learning model is traditionally considered robust if its prediction remains (almost) constant under input perturbations with small norm. However, real-world tasks like molecular property prediction or point cloud segmentation have inherent equivariances, such as rotation or permutation equivariance. In such t... | null |
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow | https://papers.nips.cc/paper_files/paper/2023/hash/00ed9ab006311be67879ecef8f80d7c5-Abstract-Conference.html | Zhaoying Pan, Daniel Geng, Andrew Owens | https://papers.nips.cc/paper_files/paper/2023/hash/00ed9ab006311be67879ecef8f80d7c5-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22090-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/00ed9ab006311be67879ecef8f80d7c5-Paper-Conference.pdf | null | This paper presents a simple, self-supervised method for magnifying subtle motions in video: given an input video and a magnification factor, we manipulate the video such that its new optical flow is scaled by the desired amount. To train our model, we propose a loss function that estimates the optical flow of the gene... | null |
TexQ: Zero-shot Network Quantization with Texture Feature Distribution Calibration | https://papers.nips.cc/paper_files/paper/2023/hash/0113ef4642264adc2e6924a3cbbdf532-Abstract-Conference.html | Xinrui Chen, Yizhi Wang, Renao YAN, Yiqing Liu, Tian Guan, Yonghong He | https://papers.nips.cc/paper_files/paper/2023/hash/0113ef4642264adc2e6924a3cbbdf532-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20087-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0113ef4642264adc2e6924a3cbbdf532-Paper-Conference.pdf | null | Quantization is an effective way to compress neural networks. By reducing the bit width of the parameters, the processing efficiency of neural network models at edge devices can be notably improved. Most conventional quantization methods utilize real datasets to optimize quantization parameters and fine-tune. Due to th... | null |
Ambient Diffusion: Learning Clean Distributions from Corrupted Data | https://papers.nips.cc/paper_files/paper/2023/hash/012af729c5d14d279581fc8a5db975a1-Abstract-Conference.html | Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam Klivans | https://papers.nips.cc/paper_files/paper/2023/hash/012af729c5d14d279581fc8a5db975a1-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21484-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/012af729c5d14d279581fc8a5db975a1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/012af729c5d14d279581fc8a5db975a1-Supplemental-Conference.zip | We present the first diffusion-based framework that can learn an unknown distribution using only highly-corrupted samples. This problem arises in scientific applications where access to uncorrupted samples is impossible or expensive to acquire. Another benefit of our approach is the ability to train generative models t... | null |
Scalable Membership Inference Attacks via Quantile Regression | https://papers.nips.cc/paper_files/paper/2023/hash/01328d0767830e73a612f9073e9ff15f-Abstract-Conference.html | Martin Bertran, Shuai Tang, Aaron Roth, Michael Kearns, Jamie H. Morgenstern, Steven Z. Wu | https://papers.nips.cc/paper_files/paper/2023/hash/01328d0767830e73a612f9073e9ff15f-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20306-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/01328d0767830e73a612f9073e9ff15f-Paper-Conference.pdf | null | Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis testing problem. The most effective existing attacks estimate the distribution of some test statistic (usuall... | null |
ESSEN: Improving Evolution State Estimation for Temporal Networks using Von Neumann Entropy | https://papers.nips.cc/paper_files/paper/2023/hash/0147d967a5db3b8dde08d2a327b24568-Abstract-Conference.html | Qiyao Huang, Yingyue Zhang, Zhihong Zhang, Edwin Hancock | https://papers.nips.cc/paper_files/paper/2023/hash/0147d967a5db3b8dde08d2a327b24568-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19868-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0147d967a5db3b8dde08d2a327b24568-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0147d967a5db3b8dde08d2a327b24568-Supplemental-Conference.pdf | Temporal networks are widely used as abstract graph representations for real-world dynamic systems. Indeed, recognizing the network evolution states is crucial in understanding and analyzing temporal networks. For instance, social networks will generate the clustering and formation of tightly-knit groups or communities... | null |
Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model | https://papers.nips.cc/paper_files/paper/2023/hash/015a8c69bedcb0a7b2ed2e1678f34399-Abstract-Conference.html | Hui GUO, Boyu Wang, Grace Yi | https://papers.nips.cc/paper_files/paper/2023/hash/015a8c69bedcb0a7b2ed2e1678f34399-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20354-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/015a8c69bedcb0a7b2ed2e1678f34399-Paper-Conference.pdf | null | The predictive ability of supervised learning algorithms hinges on the quality of annotated examples, whose labels often come from multiple crowdsourced annotators with diverse expertise. To aggregate noisy crowdsourced annotations, many existing methods employ an annotator-specific instance-independent noise transitio... | null |
Diffused Task-Agnostic Milestone Planner | https://papers.nips.cc/paper_files/paper/2023/hash/0163ca1c69f848e766cfb0b7bb7e17f4-Abstract-Conference.html | Mineui Hong, Minjae Kang, Songhwai Oh | https://papers.nips.cc/paper_files/paper/2023/hash/0163ca1c69f848e766cfb0b7bb7e17f4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20632-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0163ca1c69f848e766cfb0b7bb7e17f4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0163ca1c69f848e766cfb0b7bb7e17f4-Supplemental-Conference.zip | Addressing decision-making problems using sequence modeling to predict future trajectories shows promising results in recent years.In this paper, we take a step further to leverage the sequence predictive method in wider areas such as long-term planning, vision-based control, and multi-task decision-making.To this end,... | null |
Task-aware Distributed Source Coding under Dynamic Bandwidth | https://papers.nips.cc/paper_files/paper/2023/hash/016c63403370d81c24c1ca0123de6cfa-Abstract-Conference.html | Po-han Li, Sravan Kumar Ankireddy, Ruihan (Philip) Zhao, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Ufuk Topcu, Sandeep Chinchali, Hyeji Kim | https://papers.nips.cc/paper_files/paper/2023/hash/016c63403370d81c24c1ca0123de6cfa-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20137-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/016c63403370d81c24c1ca0123de6cfa-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/016c63403370d81c24c1ca0123de6cfa-Supplemental-Conference.pdf | Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data and transmits them to a central node. A decoder at the central node decompresses and passes the data to a pre-trained machine learning-based ... | null |
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation | https://papers.nips.cc/paper_files/paper/2023/hash/01772a8b0420baec00c4d59fe2fbace6-Abstract-Conference.html | Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljacic | https://papers.nips.cc/paper_files/paper/2023/hash/01772a8b0420baec00c4d59fe2fbace6-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19514-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/01772a8b0420baec00c4d59fe2fbace6-Paper-Conference.pdf | null | Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology. However, due to the exponentially growing size of the Hilbert space with respect to the particle number, a direct simulation is intractable. While repr... | null |
Causal Effect Identification in Uncertain Causal Networks | https://papers.nips.cc/paper_files/paper/2023/hash/017c897b4d85a744f345ccbf9d71e501-Abstract-Conference.html | Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian, Matthew Vowels, Jalal Etesami, Negar Kiyavash | https://papers.nips.cc/paper_files/paper/2023/hash/017c897b4d85a744f345ccbf9d71e501-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21831-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/017c897b4d85a744f345ccbf9d71e501-Paper-Conference.pdf | null | Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a correctly specified causal structure. In this work, we study the setti... | null |
FAST: a Fused and Accurate Shrinkage Tree for Heterogeneous Treatment Effects Estimation | https://papers.nips.cc/paper_files/paper/2023/hash/01830c92c6558179fa6d7fb1edff692c-Abstract-Conference.html | Jia Gu, Caizhi Tang, Han Yan, Qing Cui, Longfei Li, Jun Zhou | https://papers.nips.cc/paper_files/paper/2023/hash/01830c92c6558179fa6d7fb1edff692c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20615-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/01830c92c6558179fa6d7fb1edff692c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/01830c92c6558179fa6d7fb1edff692c-Supplemental-Conference.pdf | This paper proposes a novel strategy for estimating the heterogeneous treatment effect called the Fused and Accurate Shrinkage Tree ($\mathrm{FAST}$). Our approach utilizes both trial and observational data to improve the accuracy and robustness of the estimator. Inspired by the concept of shrinkage estimation in stat... | null |
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond | https://papers.nips.cc/paper_files/paper/2023/hash/01b681025fdbda8e935a66cc5bb6e9de-Abstract-Conference.html | Oleg Platonov, Denis Kuznedelev, Artem Babenko, Liudmila Prokhorenkova | https://papers.nips.cc/paper_files/paper/2023/hash/01b681025fdbda8e935a66cc5bb6e9de-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20330-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/01b681025fdbda8e935a66cc5bb6e9de-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/01b681025fdbda8e935a66cc5bb6e9de-Supplemental-Conference.zip | Homophily is a graph property describing the tendency of edges to connect similar nodes; the opposite is called heterophily. It is often believed that heterophilous graphs are challenging for standard message-passing graph neural networks (GNNs), and much effort has been put into developing efficient methods for this s... | null |
Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation | https://papers.nips.cc/paper_files/paper/2023/hash/01d64478381c33e29ed611f1719f5a37-Abstract-Conference.html | Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma | https://papers.nips.cc/paper_files/paper/2023/hash/01d64478381c33e29ed611f1719f5a37-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22270-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/01d64478381c33e29ed611f1719f5a37-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/01d64478381c33e29ed611f1719f5a37-Supplemental-Conference.zip | The generation of 3D molecules requires simultaneously deciding the categorical features (atom types) and continuous features (atom coordinates). Deep generative models, especially Diffusion Models (DMs), have demonstrated effectiveness in generating feature-rich geometries. However, existing DMs typically suffer from ... | null |
Hyperbolic VAE via Latent Gaussian Distributions | https://papers.nips.cc/paper_files/paper/2023/hash/01ecd39ca49ddecc5729ca996304781b-Abstract-Conference.html | Seunghyuk Cho, Juyong Lee, Dongwoo Kim | https://papers.nips.cc/paper_files/paper/2023/hash/01ecd39ca49ddecc5729ca996304781b-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22775-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/01ecd39ca49ddecc5729ca996304781b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/01ecd39ca49ddecc5729ca996304781b-Supplemental-Conference.zip | We propose a Gaussian manifold variational auto-encoder (GM-VAE) whose latent space consists of a set of Gaussian distributions. It is known that the set of the univariate Gaussian distributions with the Fisher information metric form a hyperbolic space, which we call a Gaussian manifold. To learn the VAE endowed with ... | null |
A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories | https://papers.nips.cc/paper_files/paper/2023/hash/0203f489345567b4a048c38f507cdbfa-Abstract-Conference.html | Kai Yan, Alex Schwing, Yu-Xiong Wang | https://papers.nips.cc/paper_files/paper/2023/hash/0203f489345567b4a048c38f507cdbfa-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20292-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0203f489345567b4a048c38f507cdbfa-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0203f489345567b4a048c38f507cdbfa-Supplemental-Conference.zip | Offline imitation from observations aims to solve MDPs where only task-specific expert states and task-agnostic non-expert state-action pairs are available. Offline imitation is useful in real-world scenarios where arbitrary interactions are costly and expert actions are unavailable. The state-of-the-art ‘DIstribution ... | null |
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training | https://papers.nips.cc/paper_files/paper/2023/hash/0207c9ea9faf66c6e892c3fa3c167b75-Abstract-Conference.html | Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, DAVID DOERMANN, Mingchen Gao | https://papers.nips.cc/paper_files/paper/2023/hash/0207c9ea9faf66c6e892c3fa3c167b75-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22026-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0207c9ea9faf66c6e892c3fa3c167b75-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0207c9ea9faf66c6e892c3fa3c167b75-Supplemental-Conference.zip | Data-Free Model Extraction (DFME) aims to clone a black-box model without knowing its original training data distribution, making it much easier for attackers to steal commercial models. Defense against DFME faces several challenges: (i) effectiveness; (ii) efficiency; (iii) no prior on the attacker's query data distri... | null |
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows | https://papers.nips.cc/paper_files/paper/2023/hash/020ad0ac6a1974e6748e4a5a48110a07-Abstract-Conference.html | David Skrill, Samuel Norman-Haignere | https://papers.nips.cc/paper_files/paper/2023/hash/020ad0ac6a1974e6748e4a5a48110a07-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22176-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/020ad0ac6a1974e6748e4a5a48110a07-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/020ad0ac6a1974e6748e4a5a48110a07-Supplemental-Conference.pdf | Modern language models excel at integrating across long temporal scales needed to encode linguistic meaning and show non-trivial similarities to biological neural systems. Prior work suggests that human brain responses to language exhibit hierarchically organized "integration windows" that substantially constrain the o... | null |
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? | https://papers.nips.cc/paper_files/paper/2023/hash/022ca1bed6b574b962c48a2856eb207b-Abstract-Conference.html | Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier | https://papers.nips.cc/paper_files/paper/2023/hash/022ca1bed6b574b962c48a2856eb207b-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20933-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/022ca1bed6b574b962c48a2856eb207b-Paper-Conference.pdf | null | We present the largest and most comprehensive empirical study of pre-trained visual representations (PVRs) or visual ‘foundation models’ for Embodied AI. First, we curate CortexBench, consisting of 17 different tasks spanning locomotion, navigation, dexterous, and mobile manipulation. Next, we systematically evaluate e... | null |
Belief Projection-Based Reinforcement Learning for Environments with Delayed Feedback | https://papers.nips.cc/paper_files/paper/2023/hash/0252a434b18962c94910c07cd9a7fecc-Abstract-Conference.html | Jangwon Kim, Hangyeol Kim, Jiwook Kang, Jongchan Baek, Soohee Han | https://papers.nips.cc/paper_files/paper/2023/hash/0252a434b18962c94910c07cd9a7fecc-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21787-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0252a434b18962c94910c07cd9a7fecc-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0252a434b18962c94910c07cd9a7fecc-Supplemental-Conference.zip | We present a novel actor-critic algorithm for an environment with delayed feedback, which addresses the state-space explosion problem of conventional approaches. Conventional approaches use an augmented state constructed from the last observed state and actions executed since visiting the last observed state. Using the... | null |
Batchnorm Allows Unsupervised Radial Attacks | https://papers.nips.cc/paper_files/paper/2023/hash/0266d95023740481d22d437aa8aba0e9-Abstract-Conference.html | Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart | https://papers.nips.cc/paper_files/paper/2023/hash/0266d95023740481d22d437aa8aba0e9-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21559-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0266d95023740481d22d437aa8aba0e9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0266d95023740481d22d437aa8aba0e9-Supplemental-Conference.zip | The construction of adversarial examples usually requires the existence of soft or hard labels for each instance, with respect to which a loss gradient provides the signal for construction of the example. We show that for batch normalized deep image recognition architectures, intermediate latents that are produced afte... | null |
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models | https://papers.nips.cc/paper_files/paper/2023/hash/02687e7b22abc64e651be8da74ec610e-Abstract-Conference.html | Yichao Cao, Qingfei Tang, Xiu Su, Song Chen, Shan You, Xiaobo Lu, Chang Xu | https://papers.nips.cc/paper_files/paper/2023/hash/02687e7b22abc64e651be8da74ec610e-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20272-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/02687e7b22abc64e651be8da74ec610e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/02687e7b22abc64e651be8da74ec610e-Supplemental-Conference.pdf | Human-object interaction (HOI) detection aims to comprehend the intricate relationships between humans and objects, predicting triplets, and serving as the foundation for numerous computer vision tasks. The complexity and diversity of human-object interactions in the real world, however, pose significant challenges fo... | null |
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models | https://papers.nips.cc/paper_files/paper/2023/hash/02763667a5761ff92bb15d8751bcd223-Abstract-Conference.html | Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee | https://papers.nips.cc/paper_files/paper/2023/hash/02763667a5761ff92bb15d8751bcd223-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19842-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/02763667a5761ff92bb15d8751bcd223-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/02763667a5761ff92bb15d8751bcd223-Supplemental-Conference.zip | We focus on the task of learning a single index model $\sigma(w^\star \cdot x)$ with respect to the isotropic Gaussian distribution in $d$ dimensions. Prior work has shown that the sample complexity of learning $w^\star$ is governed by the information exponent $k^\star$ of the link function $\sigma$, which is defined a... | null |
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models | https://papers.nips.cc/paper_files/paper/2023/hash/027e86facfe7c1ea52ca1fca7bc1402b-Abstract-Conference.html | Alexander Reisach, Myriam Tami, Christof Seiler, Antoine Chambaz, Sebastian Weichwald | https://papers.nips.cc/paper_files/paper/2023/hash/027e86facfe7c1ea52ca1fca7bc1402b-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19690-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/027e86facfe7c1ea52ca1fca7bc1402b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/027e86facfe7c1ea52ca1fca7bc1402b-Supplemental-Conference.zip | Additive Noise Models (ANMs) are a common model class for causal discovery from observational data. Due to a lack of real-world data for which an underlying ANM is known, ANMs with randomly sampled parameters are commonly used to simulate data for the evaluation of causal discovery algorithms. While some parameters may... | null |
PROTES: Probabilistic Optimization with Tensor Sampling | https://papers.nips.cc/paper_files/paper/2023/hash/028957869e560af14243ac37663a471e-Abstract-Conference.html | Anastasiia Batsheva, Andrei Chertkov, Gleb Ryzhakov, Ivan Oseledets | https://papers.nips.cc/paper_files/paper/2023/hash/028957869e560af14243ac37663a471e-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21075-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/028957869e560af14243ac37663a471e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/028957869e560af14243ac37663a471e-Supplemental-Conference.zip | We developed a new method PROTES for black-box optimization, which is based on the probabilistic sampling from a probability density function given in the low-parametric tensor train format. We tested it on complex multidimensional arrays and discretized multivariable functions taken, among others, from real-world appl... | null |
Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability | https://papers.nips.cc/paper_files/paper/2023/hash/028fcbcf85435d39a40c4d61b42c99a4-Abstract-Conference.html | Junqi Gao, Biqing Qi, Yao Li, Zhichang Guo, Dong Li, Yuming Xing, Dazhi Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/028fcbcf85435d39a40c4d61b42c99a4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19716-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/028fcbcf85435d39a40c4d61b42c99a4-Paper-Conference.pdf | null | The transferability of adversarial perturbations provides an effective shortcut for black-box attacks. Targeted perturbations have greater practicality but are more difficult to transfer between models. In this paper, we experimentally and theoretically demonstrated that neural networks trained on the same dataset have... | null |
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent | https://papers.nips.cc/paper_files/paper/2023/hash/029df12a9363313c3e41047844ecad94-Abstract-Conference.html | Ziniu Hu, Ahmet Iscen, Chen Sun, Kai-Wei Chang, Yizhou Sun, David Ross, Cordelia Schmid, Alireza Fathi | https://papers.nips.cc/paper_files/paper/2023/hash/029df12a9363313c3e41047844ecad94-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19636-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/029df12a9363313c3e41047844ecad94-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/029df12a9363313c3e41047844ecad94-Supplemental-Conference.pdf | In this paper, we propose an autonomous information seeking visual question answering framework, AVIS. Our method leverages a Large Language Model (LLM) to dynamically strategize the utilization of external tools and to investigate their outputs via tree search, thereby acquiring the indispensable knowledge needed to p... | null |
Conformal Prediction Sets for Ordinal Classification | https://papers.nips.cc/paper_files/paper/2023/hash/029f699912bf3db747fe110948cc6169-Abstract-Conference.html | Prasenjit Dey, Srujana Merugu, Sivaramakrishnan R Kaveri | https://papers.nips.cc/paper_files/paper/2023/hash/029f699912bf3db747fe110948cc6169-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21757-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/029f699912bf3db747fe110948cc6169-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/029f699912bf3db747fe110948cc6169-Supplemental-Conference.pdf | Ordinal classification (OC), i.e., labeling instances along classes with a natural ordering, is common in multiple applications such as size or budget based recommendations and disease severity labeling. Often in practical scenarios, it is desirable to obtain a small set of likely classes with a guaranteed high chanc... | null |
Minimax-Optimal Location Estimation | https://papers.nips.cc/paper_files/paper/2023/hash/02a589ef9a4f6f1e2dcc1cfb3b978a51-Abstract-Conference.html | Shivam Gupta, Jasper Lee, Eric Price, Paul Valiant | https://papers.nips.cc/paper_files/paper/2023/hash/02a589ef9a4f6f1e2dcc1cfb3b978a51-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22256-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/02a589ef9a4f6f1e2dcc1cfb3b978a51-Paper-Conference.pdf | null | Location estimation is one of the most basic questions in parametric statistics. Suppose we have a known distribution density $f$, and we get $n$ i.i.d. samples from $f(x-\mu)$ for some unknown shift $\mu$.The task is to estimate $\mu$ to high accuracy with high probability.The maximum likelihood estimator (MLE) is kno... | null |
Tight Bounds for Volumetric Spanners and Applications | https://papers.nips.cc/paper_files/paper/2023/hash/02a92b52670752daf17b53f04f1ab405-Abstract-Conference.html | Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian | https://papers.nips.cc/paper_files/paper/2023/hash/02a92b52670752daf17b53f04f1ab405-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19739-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/02a92b52670752daf17b53f04f1ab405-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/02a92b52670752daf17b53f04f1ab405-Supplemental-Conference.pdf | Given a set of points of interest, a volumetric spanner is a subset of the points using which all the points can be expressed using "small" coefficients (measured in an appropriate norm). Formally, given a set of vectors $X = [v_1, v_2, \dots, v_n]$, the goal is to find $T \subseteq [n]$ such that every $v \in X$ can b... | null |
Learning better with Dale’s Law: A Spectral Perspective | https://papers.nips.cc/paper_files/paper/2023/hash/02dd0db10c40092de3d9ec2508d12f60-Abstract-Conference.html | Pingsheng Li, Jonathan Cornford, Arna Ghosh, Blake Richards | https://papers.nips.cc/paper_files/paper/2023/hash/02dd0db10c40092de3d9ec2508d12f60-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22650-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/02dd0db10c40092de3d9ec2508d12f60-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/02dd0db10c40092de3d9ec2508d12f60-Supplemental-Conference.pdf | Most recurrent neural networks (RNNs) do not include a fundamental constraint of real neural circuits: Dale's Law, which implies that neurons must be excitatory (E) or inhibitory (I). Dale's Law is generally absent from RNNs because simply partitioning a standard network's units into E and I populations impairs learnin... | null |
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel | https://papers.nips.cc/paper_files/paper/2023/hash/02dec8877fb7c6aa9a79f81661baca7c-Abstract-Conference.html | Valerii Likhosherstov, Krzysztof M Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller | https://papers.nips.cc/paper_files/paper/2023/hash/02dec8877fb7c6aa9a79f81661baca7c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20164-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/02dec8877fb7c6aa9a79f81661baca7c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/02dec8877fb7c6aa9a79f81661baca7c-Supplemental-Conference.zip | The problem of efficient approximation of a linear operator induced by the Gaussian or softmax kernel is often addressed using random features (RFs) which yield an unbiased approximation of the operator's result. Such operators emerge in important applications ranging from kernel methods to efficient Transformers. We p... | null |
Projection-Free Online Convex Optimization via Efficient Newton Iterations | https://papers.nips.cc/paper_files/paper/2023/hash/03261886741f1f21f52f2a2d570616a2-Abstract-Conference.html | Khashayar Gatmiry, Zak Mhammedi | https://papers.nips.cc/paper_files/paper/2023/hash/03261886741f1f21f52f2a2d570616a2-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22091-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/03261886741f1f21f52f2a2d570616a2-Paper-Conference.pdf | null | This paper presents new projection-free algorithms for Online Convex Optimization (OCO) over a convex domain $\mathcal{K} \subset \mathbb{R}^d$. Classical OCO algorithms (such as Online Gradient Descent) typically need to perform Euclidean projections onto the convex set $\mathcal{K}$ to ensure feasibility of their ite... | null |
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals | https://papers.nips.cc/paper_files/paper/2023/hash/034d7bfeace2a9a258648b16fc626298-Abstract-Conference.html | Yue Wu, Yewen Fan, Paul Pu Liang, Amos Azaria, Yuanzhi Li, Tom M. Mitchell | https://papers.nips.cc/paper_files/paper/2023/hash/034d7bfeace2a9a258648b16fc626298-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20787-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/034d7bfeace2a9a258648b16fc626298-Paper-Conference.pdf | null | High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructured text documents, e.g., instruction manuals. Instruction manuals and wiki pages are among the most abundant data that could inform agents ... | null |
Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization | https://papers.nips.cc/paper_files/paper/2023/hash/0354767c6386386be17cabe4fc59711b-Abstract-Conference.html | Kaiyue Wen, Zhiyuan Li, Tengyu Ma | https://papers.nips.cc/paper_files/paper/2023/hash/0354767c6386386be17cabe4fc59711b-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21224-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0354767c6386386be17cabe4fc59711b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0354767c6386386be17cabe4fc59711b-Supplemental-Conference.zip | Despite extensive studies, the underlying reason as to why overparameterizedneural networks can generalize remains elusive. Existing theory shows that common stochastic optimizers prefer flatter minimizers of the training loss, and thusa natural potential explanation is that flatness implies generalization. This workcr... | null |
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales | https://papers.nips.cc/paper_files/paper/2023/hash/03600ae6c3392fd65ad7c3a90c6f7ce8-Abstract-Conference.html | Nikhil Vyas, Alexander Atanasov, Blake Bordelon, Depen Morwani, Sabarish Sainathan, Cengiz Pehlevan | https://papers.nips.cc/paper_files/paper/2023/hash/03600ae6c3392fd65ad7c3a90c6f7ce8-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22545-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/03600ae6c3392fd65ad7c3a90c6f7ce8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/03600ae6c3392fd65ad7c3a90c6f7ce8-Supplemental-Conference.zip | We study the effect of width on the dynamics of feature-learning neural networks across a variety of architectures and datasets. Early in training, wide neural networks trained on online data have not only identical loss curves but also agree in their point-wise test predictions throughout training. For simple tasks su... | null |
Taylor TD-learning | https://papers.nips.cc/paper_files/paper/2023/hash/036912a83bdbb1fd792baf6532f102d8-Abstract-Conference.html | Michele Garibbo, Maxime Robeyns, Laurence Aitchison | https://papers.nips.cc/paper_files/paper/2023/hash/036912a83bdbb1fd792baf6532f102d8-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22597-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/036912a83bdbb1fd792baf6532f102d8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/036912a83bdbb1fd792baf6532f102d8-Supplemental-Conference.pdf | Many reinforcement learning approaches rely on temporal-difference (TD) learning to learn a critic.However, TD-learning updates can be high variance.Here, we introduce a model-based RL framework, Taylor TD, which reduces this variance in continuous state-action settings. Taylor TD uses a first-order Taylor series expan... | null |
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability | https://papers.nips.cc/paper_files/paper/2023/hash/03a9a9c1e15850439653bb971a4ad4b3-Abstract-Conference.html | Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis | https://papers.nips.cc/paper_files/paper/2023/hash/03a9a9c1e15850439653bb971a4ad4b3-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21076-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/03a9a9c1e15850439653bb971a4ad4b3-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/03a9a9c1e15850439653bb971a4ad4b3-Supplemental-Conference.zip | Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the likelihood function is only implicitly established by a simulator posing the need for simulation-based inference (SBI). However, the existin... | null |
Agnostic Multi-Group Active Learning | https://papers.nips.cc/paper_files/paper/2023/hash/03b1043052700b1a471996b0baf309d4-Abstract-Conference.html | Nicholas Rittler, Kamalika Chaudhuri | https://papers.nips.cc/paper_files/paper/2023/hash/03b1043052700b1a471996b0baf309d4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22161-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/03b1043052700b1a471996b0baf309d4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/03b1043052700b1a471996b0baf309d4-Supplemental-Conference.pdf | Inspired by the problem of improving classification accuracy on rare or hard subsets of a population, there has been recent interest in models of learning where the goal is to generalize to a collection of distributions, each representing a ``group''. We consider a variant of this problem from the perspective of active... | null |
Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration | https://papers.nips.cc/paper_files/paper/2023/hash/03b13b0db740b95cb741e007178ef5e5-Abstract-Conference.html | Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen, Gang Niu, Xiaofeng Zhu | https://papers.nips.cc/paper_files/paper/2023/hash/03b13b0db740b95cb741e007178ef5e5-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20190-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/03b13b0db740b95cb741e007178ef5e5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/03b13b0db740b95cb741e007178ef5e5-Supplemental-Conference.pdf | Recently, numerous studies have demonstrated the effectiveness of contrastive learning (CL), which learns feature representations by pulling in positive samples while pushing away negative samples. Many successes of CL lie in that there exists semantic consistency between data augmentations of the same instance. In mul... | null |
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features | https://papers.nips.cc/paper_files/paper/2023/hash/03df5246cc78af497940338dd3eacbaa-Abstract-Conference.html | Mingli Zhu, Shaokui Wei, Hongyuan Zha, Baoyuan Wu | https://papers.nips.cc/paper_files/paper/2023/hash/03df5246cc78af497940338dd3eacbaa-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22465-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/03df5246cc78af497940338dd3eacbaa-Paper-Conference.pdf | null | Recent studies have demonstrated the susceptibility of deep neural networks to backdoor attacks. Given a backdoored model, its prediction of a poisoned sample with trigger will be dominated by the trigger information, though trigger information and benign information coexist. Inspired by the mechanism of the optical po... | null |
Tools for Verifying Neural Models' Training Data | https://papers.nips.cc/paper_files/paper/2023/hash/03e33e1f62e3302b47fe1d38a235921e-Abstract-Conference.html | Dami Choi, Yonadav Shavit, David K. Duvenaud | https://papers.nips.cc/paper_files/paper/2023/hash/03e33e1f62e3302b47fe1d38a235921e-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22555-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/03e33e1f62e3302b47fe1d38a235921e-Paper-Conference.pdf | null | It is important that consumers and regulators can verify the provenance of large neural models to evaluate their capabilities and risks. We introduce the concept of a "Proof-of-Training-Data": any protocol that allows a model trainer to convince a Verifier of the training data that produced a set of model weights. Such... | null |
Towards Higher Ranks via Adversarial Weight Pruning | https://papers.nips.cc/paper_files/paper/2023/hash/040ace837dd270a87055bb10dd7c0392-Abstract-Conference.html | Yuchuan Tian, Hanting Chen, Tianyu Guo, Chao Xu, Yunhe Wang | https://papers.nips.cc/paper_files/paper/2023/hash/040ace837dd270a87055bb10dd7c0392-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21480-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/040ace837dd270a87055bb10dd7c0392-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/040ace837dd270a87055bb10dd7c0392-Supplemental-Conference.pdf | Convolutional Neural Networks (CNNs) are hard to deploy on edge devices due to its high computation and storage complexities. As a common practice for model compression, network pruning consists of two major categories: unstructured and structured pruning, where unstructured pruning constantly performs better. However,... | null |
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective | https://papers.nips.cc/paper_files/paper/2023/hash/040d3b6af368bf71f952c18da5713b48-Abstract-Conference.html | Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama | https://papers.nips.cc/paper_files/paper/2023/hash/040d3b6af368bf71f952c18da5713b48-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19692-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/040d3b6af368bf71f952c18da5713b48-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/040d3b6af368bf71f952c18da5713b48-Supplemental-Conference.pdf | Weight decay is a simple yet powerful regularization technique that has been very widely used in training of deep neural networks (DNNs). While weight decay has attracted much attention, previous studies fail to discover some overlooked pitfalls on large gradient norms resulted by weight decay. In this paper, we discov... | null |
Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis | https://papers.nips.cc/paper_files/paper/2023/hash/04261fce1705c4f02f062866717d592a-Abstract-Conference.html | Yulhwa Kim, Dongwon Jo, Hyesung Jeon, Taesu Kim, Daehyun Ahn, Hyungjun Kim, jae-joon kim | https://papers.nips.cc/paper_files/paper/2023/hash/04261fce1705c4f02f062866717d592a-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21515-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/04261fce1705c4f02f062866717d592a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/04261fce1705c4f02f062866717d592a-Supplemental-Conference.zip | While diffusion models have demonstrated exceptional image generation capabilities, the iterative noise estimation process required for these models is compute-intensive and their practical implementation is limited by slow sampling speeds. In this paper, we propose a novel approach to speed up the noise estimation net... | null |
Adversarial Model for Offline Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2023/hash/0429ececfb199efc93182990169e73bb-Abstract-Conference.html | Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng | https://papers.nips.cc/paper_files/paper/2023/hash/0429ececfb199efc93182990169e73bb-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19741-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0429ececfb199efc93182990169e73bb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0429ececfb199efc93182990169e73bb-Supplemental-Conference.zip | We propose a novel model-based offline Reinforcement Learning (RL) framework, called Adversarial Model for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitrary reference policy regardless of data coverage. ARMOR is designed to optimize policies for the worst-case perfor... | null |
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function | https://papers.nips.cc/paper_files/paper/2023/hash/043f0503c4f652c737add3690aa5d12c-Abstract-Conference.html | man zhou, Naishan Zheng, Yuan Xu, Chun-Le Guo, Chongyi Li | https://papers.nips.cc/paper_files/paper/2023/hash/043f0503c4f652c737add3690aa5d12c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20909-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/043f0503c4f652c737add3690aa5d12c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/043f0503c4f652c737add3690aa5d12c-Supplemental-Conference.pdf | The blooming progress made in deep learning-based image restoration has been largely attributed to the availability of high-quality, large-scale datasets and advanced network structures. However, optimization functions such as L1 and L2 are still de facto. In this study, we propose to investigate new optimization func... | null |
Passive learning of active causal strategies in agents and language models | https://papers.nips.cc/paper_files/paper/2023/hash/045c87def0c02e3ad0d3d849766d7f1e-Abstract-Conference.html | Andrew Lampinen, Stephanie Chan, Ishita Dasgupta, Andrew Nam, Jane Wang | https://papers.nips.cc/paper_files/paper/2023/hash/045c87def0c02e3ad0d3d849766d7f1e-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22193-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/045c87def0c02e3ad0d3d849766d7f1e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/045c87def0c02e3ad0d3d849766d7f1e-Supplemental-Conference.pdf | What can be learned about causality and experimentation from passive data? This question is salient given recent successes of passively-trained language models in interactive domains such as tool use. Passive learning is inherently limited. However, we show that purely passive learning can in fact allow an agent to lea... | null |
Zero-Regret Performative Prediction Under Inequality Constraints | https://papers.nips.cc/paper_files/paper/2023/hash/047397849f63b4fcfced4ff720159f3d-Abstract-Conference.html | Wenjing YAN, Xuanyu Cao | https://papers.nips.cc/paper_files/paper/2023/hash/047397849f63b4fcfced4ff720159f3d-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20204-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/047397849f63b4fcfced4ff720159f3d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/047397849f63b4fcfced4ff720159f3d-Supplemental-Conference.pdf | Performative prediction is a recently proposed framework where predictions guide decision-making and hence influence future data distributions. Such performative phenomena are ubiquitous in various areas, such as transportation, finance, public policy, and recommendation systems. To date, work on performative predictio... | null |
Towards Free Data Selection with General-Purpose Models | https://papers.nips.cc/paper_files/paper/2023/hash/047682108c3b053c61ad2da5a6057b4e-Abstract-Conference.html | Yichen Xie, Mingyu Ding, Masayoshi TOMIZUKA, Wei Zhan | https://papers.nips.cc/paper_files/paper/2023/hash/047682108c3b053c61ad2da5a6057b4e-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21335-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/047682108c3b053c61ad2da5a6057b4e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/047682108c3b053c61ad2da5a6057b4e-Supplemental-Conference.pdf | A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. However, current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selec... | null |
Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems | https://papers.nips.cc/paper_files/paper/2023/hash/04bd683d5428d91c5fbb5a7d2c27064d-Abstract-Conference.html | Junyi Li, Feihu Huang, Heng Huang | https://papers.nips.cc/paper_files/paper/2023/hash/04bd683d5428d91c5fbb5a7d2c27064d-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20233-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/04bd683d5428d91c5fbb5a7d2c27064d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/04bd683d5428d91c5fbb5a7d2c27064d-Supplemental-Conference.pdf | Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms. However, its application in the Federated Learning setting remains relatively underexplored, and the impact of Federated Learning's inherent challenges on the convergence of bilevel algorithms remain obscure.In this wor... | null |
Partial Multi-Label Learning with Probabilistic Graphical Disambiguation | https://papers.nips.cc/paper_files/paper/2023/hash/04e05ba5cbc36044f6499d1edf15247e-Abstract-Conference.html | Jun-Yi Hang, Min-Ling Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/04e05ba5cbc36044f6499d1edf15247e-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21623-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/04e05ba5cbc36044f6499d1edf15247e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/04e05ba5cbc36044f6499d1edf15247e-Supplemental-Conference.pdf | In partial multi-label learning (PML), each training example is associated with a set of candidate labels, among which only some labels are valid. As a common strategy to tackle PML problem, disambiguation aims to recover the ground-truth labeling information from such inaccurate annotations. However, existing approach... | null |
Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks | https://papers.nips.cc/paper_files/paper/2023/hash/04f61ec02d1b3a025a59d978269ce437-Abstract-Conference.html | Ryan Sullivan, Akarsh Kumar, Shengyi Huang, John Dickerson, Joseph Suarez | https://papers.nips.cc/paper_files/paper/2023/hash/04f61ec02d1b3a025a59d978269ce437-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19965-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/04f61ec02d1b3a025a59d978269ce437-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/04f61ec02d1b3a025a59d978269ce437-Supplemental-Conference.pdf | Most reinforcement learning methods rely heavily on dense, well-normalized environment rewards. DreamerV3 recently introduced a model-based method with a number of tricks that mitigate these limitations, achieving state-of-the-art on a wide range of benchmarks with a single set of hyperparameters. This result sparked d... | null |
Emergent Correspondence from Image Diffusion | https://papers.nips.cc/paper_files/paper/2023/hash/0503f5dce343a1d06d16ba103dd52db1-Abstract-Conference.html | Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, Bharath Hariharan | https://papers.nips.cc/paper_files/paper/2023/hash/0503f5dce343a1d06d16ba103dd52db1-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19892-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0503f5dce343a1d06d16ba103dd52db1-Paper-Conference.pdf | null | Finding correspondences between images is a fundamental problem in computer vision. In this paper, we show that correspondence emerges in image diffusion models without any explicit supervision. We propose a simple strategy to extract this implicit knowledge out of diffusion networks as image features, namely DIffusion... | null |
Robust Learning with Progressive Data Expansion Against Spurious Correlation | https://papers.nips.cc/paper_files/paper/2023/hash/0506ad3d1bcc8398a920db9340f27fe4-Abstract-Conference.html | Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu | https://papers.nips.cc/paper_files/paper/2023/hash/0506ad3d1bcc8398a920db9340f27fe4-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22024-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0506ad3d1bcc8398a920db9340f27fe4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0506ad3d1bcc8398a920db9340f27fe4-Supplemental-Conference.pdf | While deep learning models have shown remarkable performance in various tasks, they are susceptible to learning non-generalizable _spurious features_ rather than the core features that are genuinely correlated to the true label. In this paper, beyond existing analyses of linear models, we theoretically examine the lear... | null |
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria | https://papers.nips.cc/paper_files/paper/2023/hash/050f8591be3874b52fdac4e1060eeb29-Abstract-Conference.html | Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran | https://papers.nips.cc/paper_files/paper/2023/hash/050f8591be3874b52fdac4e1060eeb29-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20243-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/050f8591be3874b52fdac4e1060eeb29-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/050f8591be3874b52fdac4e1060eeb29-Supplemental-Conference.pdf | We study a generalization of boosting to the multiclass setting.We introduce a weak learning condition for multiclass classification that captures the original notion of weak learnability as being “slightly better than random guessing”. We give a simple and efficient boosting algorithm, that does not require realizabil... | null |
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent | https://papers.nips.cc/paper_files/paper/2023/hash/0525a72df7fb2cd943c780d059b94774-Abstract-Conference.html | Kruno Lehman, Alain Durmus, Umut Simsekli | https://papers.nips.cc/paper_files/paper/2023/hash/0525a72df7fb2cd943c780d059b94774-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19638-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0525a72df7fb2cd943c780d059b94774-Paper-Conference.pdf | null | A recent line of empirical studies has demonstrated that SGD might exhibit a heavy-tailed behavior in practical settings, and the heaviness of the tails might correlate with the overall performance. In this paper, we investigate the emergence of such heavy tails. Previous works on this problem only considered, up to ou... | null |
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow | https://papers.nips.cc/paper_files/paper/2023/hash/0534abc9e6db91683d82186ef0d68202-Abstract-Conference.html | Cameron Smith, Yilun Du, Ayush Tewari, Vincent Sitzmann | https://papers.nips.cc/paper_files/paper/2023/hash/0534abc9e6db91683d82186ef0d68202-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21853-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0534abc9e6db91683d82186ef0d68202-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0534abc9e6db91683d82186ef0d68202-Supplemental-Conference.zip | Reconstruction of 3D neural fields from posed images has emerged as a promising method for self-supervised representation learning. The key challenge preventing the deployment of these 3D scene learners on large-scale video data is their dependence on precise camera poses from structure-from-motion, which is prohibitiv... | null |
Minimum Description Length and Generalization Guarantees for Representation Learning | https://papers.nips.cc/paper_files/paper/2023/hash/054e9f9a286671ababa3213d6e59c1c2-Abstract-Conference.html | Milad Sefidgaran, Abdellatif Zaidi, Piotr Krasnowski | https://papers.nips.cc/paper_files/paper/2023/hash/054e9f9a286671ababa3213d6e59c1c2-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21923-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/054e9f9a286671ababa3213d6e59c1c2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/054e9f9a286671ababa3213d6e59c1c2-Supplemental-Conference.pdf | A major challenge in designing efficient statistical supervised learning algorithms is finding representations that perform well not only on available training samples but also on unseen data. While the study of representation learning has spurred much interest, most existing such approaches are heuristic; and very lit... | null |
From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion | https://papers.nips.cc/paper_files/paper/2023/hash/054f771d614df12fe8def8ecdbe4e8e1-Abstract-Conference.html | Robin San Roman, Yossi Adi, Antoine Deleforge, Romain Serizel, Gabriel Synnaeve, Alexandre Defossez | https://papers.nips.cc/paper_files/paper/2023/hash/054f771d614df12fe8def8ecdbe4e8e1-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21274-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/054f771d614df12fe8def8ecdbe4e8e1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/054f771d614df12fe8def8ecdbe4e8e1-Supplemental-Conference.pdf | Deep generative models can generate high-fidelity audio conditioned on varioustypes of representations (e.g., mel-spectrograms, Mel-frequency Cepstral Coefficients(MFCC)). Recently, such models have been used to synthesize audiowaveforms conditioned on highly compressed representations. Although suchmethods produce imp... | null |
Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs | https://papers.nips.cc/paper_files/paper/2023/hash/055fc19a3ce780b96cff15ffe738c1f1-Abstract-Conference.html | Rajat Vadiraj Dwaraknath, Tolga Ergen, Mert Pilanci | https://papers.nips.cc/paper_files/paper/2023/hash/055fc19a3ce780b96cff15ffe738c1f1-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21678-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/055fc19a3ce780b96cff15ffe738c1f1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/055fc19a3ce780b96cff15ffe738c1f1-Supplemental-Conference.zip | Recently, theoretical analyses of deep neural networks have broadly focused on two directions: 1) Providing insight into neural network training by SGD in the limit of infinite hidden-layer width and infinitesimally small learning rate (also known as gradient flow) via the Neural Tangent Kernel (NTK), and 2) Globally o... | null |
Birth of a Transformer: A Memory Viewpoint | https://papers.nips.cc/paper_files/paper/2023/hash/0561738a239a995c8cd2ef0e50cfa4fd-Abstract-Conference.html | Alberto Bietti, Vivien Cabannes, Diane Bouchacourt, Herve Jegou, Leon Bottou | https://papers.nips.cc/paper_files/paper/2023/hash/0561738a239a995c8cd2ef0e50cfa4fd-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19662-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0561738a239a995c8cd2ef0e50cfa4fd-Paper-Conference.pdf | null | Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their internal mechanisms in order to make them more reliable. These models appear to store vast amounts of knowledge from their training data, and... | null |
A Variational Perspective on High-Resolution ODEs | https://papers.nips.cc/paper_files/paper/2023/hash/0569458210c88d8db2985799da830d27-Abstract-Conference.html | Hoomaan Maskan, Konstantinos Zygalakis, Alp Yurtsever | https://papers.nips.cc/paper_files/paper/2023/hash/0569458210c88d8db2985799da830d27-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20103-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0569458210c88d8db2985799da830d27-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0569458210c88d8db2985799da830d27-Supplemental-Conference.zip | We consider unconstrained minimization of smooth convex functions. We propose a novel variational perspective using forced Euler-Lagrange equation that allows for studying high-resolution ODEs. Through this, we obtain a faster convergence rate for gradient norm minimization using Nesterov's accelerated gradient method.... | null |
What You See is What You Read? Improving Text-Image Alignment Evaluation | https://papers.nips.cc/paper_files/paper/2023/hash/056e8e9c8ca9929cb6cf198952bf1dbb-Abstract-Conference.html | Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni, Jonathan Herzig, Oran Lang, Eran Ofek, Idan Szpektor | https://papers.nips.cc/paper_files/paper/2023/hash/056e8e9c8ca9929cb6cf198952bf1dbb-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22359-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/056e8e9c8ca9929cb6cf198952bf1dbb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/056e8e9c8ca9929cb6cf198952bf1dbb-Supplemental-Conference.zip | Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic text-image alignment evaluation. We first introduce SeeTR... | null |
On the Robustness of Mechanism Design under Total Variation Distance | https://papers.nips.cc/paper_files/paper/2023/hash/058983528186511a74968e88a6d0ad63-Abstract-Conference.html | Anuran Makur, Marios Mertzanidis, Alexandros Psomas, Athina Terzoglou | https://papers.nips.cc/paper_files/paper/2023/hash/058983528186511a74968e88a6d0ad63-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20388-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/058983528186511a74968e88a6d0ad63-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/058983528186511a74968e88a6d0ad63-Supplemental-Conference.pdf | We study the problem of designing mechanisms when agents' valuation functions are drawn from unknown and correlated prior distributions. In particular, we are given a prior distribution $D$, and we are interested in designing a (truthful) mechanism that has good performance for all "true distributions" that are close t... | null |
A generative model of the hippocampal formation trained with theta driven local learning rules | https://papers.nips.cc/paper_files/paper/2023/hash/05ab457c7b769f01c2973e2a5ab66ad9-Abstract-Conference.html | Tom M George, Kimberly L. Stachenfeld, Caswell Barry, Claudia Clopath, Tomoki Fukai | https://papers.nips.cc/paper_files/paper/2023/hash/05ab457c7b769f01c2973e2a5ab66ad9-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22627-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05ab457c7b769f01c2973e2a5ab66ad9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/05ab457c7b769f01c2973e2a5ab66ad9-Supplemental-Conference.pdf | Advances in generative models have recently revolutionised machine learning. Meanwhile, in neuroscience, generative models have long been thought fundamental to animal intelligence. Understanding the biological mechanisms that support these processes promises to shed light on the relationship between biological and art... | null |
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2023/hash/05b63fa06784b71aab3939004e0f0a0d-Abstract-Conference.html | James Queeney, Mouhacine Benosman | https://papers.nips.cc/paper_files/paper/2023/hash/05b63fa06784b71aab3939004e0f0a0d-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19925-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05b63fa06784b71aab3939004e0f0a0d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/05b63fa06784b71aab3939004e0f0a0d-Supplemental-Conference.pdf | Many real-world domains require safe decision making in uncertain environments. In this work, we introduce a deep reinforcement learning framework for approaching this important problem. We consider a distribution over transition models, and apply a risk-averse perspective towards model uncertainty through the use of c... | null |
Optimal approximation using complex-valued neural networks | https://papers.nips.cc/paper_files/paper/2023/hash/05b69cc4c8ff6e24c5de1ecd27223d37-Abstract-Conference.html | Paul Geuchen, Felix Voigtlaender | https://papers.nips.cc/paper_files/paper/2023/hash/05b69cc4c8ff6e24c5de1ecd27223d37-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22175-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05b69cc4c8ff6e24c5de1ecd27223d37-Paper-Conference.pdf | null | Complex-valued neural networks (CVNNs) have recently shown promising empirical success, for instance for increasing the stability of recurrent neural networks and for improving the performance in tasks with complex-valued inputs, such as MRI fingerprinting. While the overwhelming success of Deep Learning in the real-va... | null |
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery | https://papers.nips.cc/paper_files/paper/2023/hash/05cf28e3d3c9a179d789c55270fe6f72-Abstract-Conference.html | Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong | https://papers.nips.cc/paper_files/paper/2023/hash/05cf28e3d3c9a179d789c55270fe6f72-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19548-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05cf28e3d3c9a179d789c55270fe6f72-Paper-Conference.pdf | null | Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However, computational challenges arise due to joint inference over combinatorial space of Directed Acyclic Graphs (DAGs) and nonlinear functions. ... | null |
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces | https://papers.nips.cc/paper_files/paper/2023/hash/05d2175de7ee637588d1b5ced8b15b32-Abstract-Conference.html | Leonard Papenmeier, Luigi Nardi, Matthias Poloczek | https://papers.nips.cc/paper_files/paper/2023/hash/05d2175de7ee637588d1b5ced8b15b32-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20766-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05d2175de7ee637588d1b5ced8b15b32-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/05d2175de7ee637588d1b5ced8b15b32-Supplemental-Conference.zip | Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces.While Bayesian optimization has recently made significant progress in solving such problems, an in-... | null |
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent | https://papers.nips.cc/paper_files/paper/2023/hash/05d6b5b6901fb57d2c287e1d3ce6d63c-Abstract-Conference.html | Lingjiong Zhu, Mert Gurbuzbalaban, Anant Raj, Umut Simsekli | https://papers.nips.cc/paper_files/paper/2023/hash/05d6b5b6901fb57d2c287e1d3ce6d63c-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21554-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05d6b5b6901fb57d2c287e1d3ce6d63c-Paper-Conference.pdf | null | Algorithmic stability is an important notion that has proven powerful for deriving generalization bounds for practical algorithms. The last decade has witnessed an increasing number of stability bounds for different algorithms applied on different classes of loss functions. While these bounds have illuminated various p... | null |
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation | https://papers.nips.cc/paper_files/paper/2023/hash/05dc08730e32441edff52b0fa6caab5f-Abstract-Conference.html | Haonan Wang, Xiaomeng Li | https://papers.nips.cc/paper_files/paper/2023/hash/05dc08730e32441edff52b0fa6caab5f-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/19625-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05dc08730e32441edff52b0fa6caab5f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/05dc08730e32441edff52b0fa6caab5f-Supplemental-Conference.pdf | Volume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data. However, the challenges and practical applications extend beyond SSL to settings such as unsupe... | null |
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis | https://papers.nips.cc/paper_files/paper/2023/hash/05e552739c2629f3324c1063a382b4bd-Abstract-Conference.html | Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/05e552739c2629f3324c1063a382b4bd-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22682-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05e552739c2629f3324c1063a382b4bd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/05e552739c2629f3324c1063a382b4bd-Supplemental-Conference.pdf | We study finite-sum distributed optimization problems involving a master node and $n-1$ local nodes under the popular $\delta$-similarity and $\mu$-strong convexity conditions. We propose two new algorithms, SVRS and AccSVRS, motivated by previous works. The non-accelerated SVRS method combines the techniques of gradie... | null |
PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models | https://papers.nips.cc/paper_files/paper/2023/hash/05f0e2fa003602db2d98ca72b79dec51-Abstract-Conference.html | Jiacheng Chen, Ruizhi Deng, Yasutaka Furukawa | https://papers.nips.cc/paper_files/paper/2023/hash/05f0e2fa003602db2d98ca72b79dec51-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21588-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05f0e2fa003602db2d98ca72b79dec51-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/05f0e2fa003602db2d98ca72b79dec51-Supplemental-Conference.pdf | This paper presents \textit{PolyDiffuse}, a novel structured reconstruction algorithm that transforms visual sensor data into polygonal shapes with Diffusion Models (DM), an emerging machinery amid exploding generative AI, while formulating reconstruction as a generation process conditioned on sensor data. The task of ... | null |
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data | https://papers.nips.cc/paper_files/paper/2023/hash/05fb0f4e645cad23e0ab59d6b9901428-Abstract-Conference.html | Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar | https://papers.nips.cc/paper_files/paper/2023/hash/05fb0f4e645cad23e0ab59d6b9901428-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20383-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05fb0f4e645cad23e0ab59d6b9901428-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/05fb0f4e645cad23e0ab59d6b9901428-Supplemental-Conference.pdf | Evaluating the performance of machine learning models on diverse and underrepresented subgroups is essential for ensuring fairness and reliability in real-world applications. However, accurately assessing model performance becomes challenging due to two main issues: (1) a scarcity of test data, especially for small sub... | null |
Rethinking the Backward Propagation for Adversarial Transferability | https://papers.nips.cc/paper_files/paper/2023/hash/05fe0c633ae41756540dba2a99a36306-Abstract-Conference.html | Wang Xiaosen, Kangheng Tong, Kun He | https://papers.nips.cc/paper_files/paper/2023/hash/05fe0c633ae41756540dba2a99a36306-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20937-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/05fe0c633ae41756540dba2a99a36306-Paper-Conference.pdf | null | Transfer-based attacks generate adversarial examples on the surrogate model, which can mislead other black-box models without access, making it promising to attack real-world applications. Recently, several works have been proposed to boost adversarial transferability, in which the surrogate model is usually overlooked... | null |
Compression with Bayesian Implicit Neural Representations | https://papers.nips.cc/paper_files/paper/2023/hash/060b2af0081a460f7f466f7f174d9052-Abstract-Conference.html | Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato | https://papers.nips.cc/paper_files/paper/2023/hash/060b2af0081a460f7f466f7f174d9052-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21267-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/060b2af0081a460f7f466f7f174d9052-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/060b2af0081a460f7f466f7f174d9052-Supplemental-Conference.pdf | Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image. Based on this view, data can be compressed by overfitting a compact neural network to its functional representation and then encoding the network weights. Howev... | null |
Towards Unbounded Machine Unlearning | https://papers.nips.cc/paper_files/paper/2023/hash/062d711fb777322e2152435459e6e9d9-Abstract-Conference.html | Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, Eleni Triantafillou | https://papers.nips.cc/paper_files/paper/2023/hash/062d711fb777322e2152435459e6e9d9-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/21511-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/062d711fb777322e2152435459e6e9d9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/062d711fb777322e2152435459e6e9d9-Supplemental-Conference.zip | Deep machine unlearning is the problem of 'removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including the key tasks of removing biases (RB), resolving confusion (RC) (caused by mislabelled data in trained models), as well as allowing users to e... | null |
Collaborative Learning via Prediction Consensus | https://papers.nips.cc/paper_files/paper/2023/hash/065e259a1d2d955e63b99aac6a3a3081-Abstract-Conference.html | Dongyang Fan, Celestine Mendler-Dünner, Martin Jaggi | https://papers.nips.cc/paper_files/paper/2023/hash/065e259a1d2d955e63b99aac6a3a3081-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22332-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/065e259a1d2d955e63b99aac6a3a3081-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/065e259a1d2d955e63b99aac6a3a3081-Supplemental-Conference.pdf | We consider a collaborative learning setting where the goal of each agent is to improve their own model by leveraging the expertise of collaborators, in addition to their own training data. To facilitate the exchange of expertise among agents, we propose a distillation-based method leveraging shared unlabeled auxiliary... | null |
Identification of Nonlinear Latent Hierarchical Models | https://papers.nips.cc/paper_files/paper/2023/hash/065ef23a944b3995de7dd4a3e203d133-Abstract-Conference.html | Lingjing Kong, Biwei Huang, Feng Xie, Eric Xing, Yuejie Chi, Kun Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/065ef23a944b3995de7dd4a3e203d133-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20693-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/065ef23a944b3995de7dd4a3e203d133-Paper-Conference.pdf | null | Identifying latent variables and causal structures from observational data is essential to many real-world applications involving biological data, medical data, and unstructured data such as images and languages. However, this task can be highly challenging, especially when observed variables are generated by causally ... | null |
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks | https://papers.nips.cc/paper_files/paper/2023/hash/0663a39baab211328fc865f91abc75ab-Abstract-Conference.html | Honghao Wei, Xin Liu, Weina Wang, Lei Ying | https://papers.nips.cc/paper_files/paper/2023/hash/0663a39baab211328fc865f91abc75ab-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22719-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/0663a39baab211328fc865f91abc75ab-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/0663a39baab211328fc865f91abc75ab-Supplemental-Conference.zip | This paper considers a class of reinforcement learning problems, which involve systems with two types of states: stochastic and pseudo-stochastic. In such systems, stochastic states follow a stochastic transition kernel while the transitions of pseudo-stochastic states are deterministic {\em given} the stochastic state... | null |
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection | https://papers.nips.cc/paper_files/paper/2023/hash/066e4dbfeccb5dc2851acd5eca584937-Abstract-Conference.html | Taehyeon Kim, Eric Lin, Junu Lee, Christian Lau, Vaikkunth Mugunthan | https://papers.nips.cc/paper_files/paper/2023/hash/066e4dbfeccb5dc2851acd5eca584937-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22736-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/066e4dbfeccb5dc2851acd5eca584937-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/066e4dbfeccb5dc2851acd5eca584937-Supplemental-Conference.pdf | Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy. Nevertheless, it faces challenges with limited high-quality labels and non-IID client data, particularly in applications like autonomous driving. To address these hurdles, we nav... | null |
On the Generalization Properties of Diffusion Models | https://papers.nips.cc/paper_files/paper/2023/hash/06abed94583030dd50abe6767bd643b1-Abstract-Conference.html | Puheng Li, Zhong Li, Huishuai Zhang, Jiang Bian | https://papers.nips.cc/paper_files/paper/2023/hash/06abed94583030dd50abe6767bd643b1-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22202-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/06abed94583030dd50abe6767bd643b1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/06abed94583030dd50abe6767bd643b1-Supplemental-Conference.pdf | Diffusion models are a class of generative models that serve to establish a stochastic transport map between an empirically observed, yet unknown, target distribution and a known prior. Despite their remarkable success in real-world applications, a theoretical understanding of their generalization capabilities remains ... | null |
Regularized Behavior Cloning for Blocking the Leakage of Past Action Information | https://papers.nips.cc/paper_files/paper/2023/hash/06b71ad997f7e3e4b2e2f2ea12e5a759-Abstract-Conference.html | Seokin Seo, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim | https://papers.nips.cc/paper_files/paper/2023/hash/06b71ad997f7e3e4b2e2f2ea12e5a759-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/20380-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/06b71ad997f7e3e4b2e2f2ea12e5a759-Paper-Conference.pdf | null | For partially observable environments, imitation learning with observation histories (ILOH) assumes that control-relevant information is sufficiently captured in the observation histories for imitating the expert actions. In the offline setting wherethe agent is required to learn to imitate without interaction with the... | null |
The Distortion of Binomial Voting Defies Expectation | https://papers.nips.cc/paper_files/paper/2023/hash/06cb881ec90a657a8f949a62f1b4ee5f-Abstract-Conference.html | Yannai A. Gonczarowski, Gregory Kehne, Ariel D. Procaccia, Ben Schiffer, Shirley Zhang | https://papers.nips.cc/paper_files/paper/2023/hash/06cb881ec90a657a8f949a62f1b4ee5f-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22281-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/06cb881ec90a657a8f949a62f1b4ee5f-Paper-Conference.pdf | null | In computational social choice, the distortion of a voting rule quantifies the degree to which the rule overcomes limited preference information to select a socially desirable outcome. This concept has been investigated extensively, but only through a worst-case lens. Instead, we study the expected distortion of voting... | null |
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models | https://papers.nips.cc/paper_files/paper/2023/hash/06d5f1fe6509b001e6d4e0ec1afd83dd-Abstract-Conference.html | Xin Li, Sima Behpour, Thang Long Doan, Wenbin He, Liang Gou, Liu Ren | https://papers.nips.cc/paper_files/paper/2023/hash/06d5f1fe6509b001e6d4e0ec1afd83dd-Abstract-Conference.html | NIPS 2023 | https://papers.nips.cc/paper_files/paper/22674-/bibtex | https://papers.nips.cc/paper_files/paper/2023/file/06d5f1fe6509b001e6d4e0ec1afd83dd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2023/file/06d5f1fe6509b001e6d4e0ec1afd83dd-Supplemental-Conference.zip | In this study, we investigate the task of data pre-selection, which aims to select instances for labeling from an unlabeled dataset through a single pass, thereby optimizing performance for undefined downstream tasks with a limited annotation budget. Previous approaches to data pre-selection relied solely on visual fea... | null |
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Check out the documentation for more information.
NIPS 2023 Accepted Paper Meta Info Dataset
This dataset is collect from the NIPS 2023 OpenReview website (https://papers.nips.cc/paper_files/paper/2023) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/nips2023). For researchers who are interested in doing analysis of NIPS 2023 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the NIPS 2023 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Meta Information of Json File of Paper
{
"title": "Scalable Membership Inference Attacks via Quantile Regression",
"url": "https://papers.nips.cc/paper_files/paper/2023/hash/01328d0767830e73a612f9073e9ff15f-Abstract-Conference.html",
"authors": "Martin Bertran, Shuai Tang, Aaron Roth, Michael Kearns, Jamie H. Morgenstern, Steven Z. Wu",
"detail_url": "https://papers.nips.cc/paper_files/paper/2023/hash/01328d0767830e73a612f9073e9ff15f-Abstract-Conference.html",
"tags": "NIPS 2023",
"Bibtex": "https://papers.nips.cc/paper_files/paper/20306-/bibtex",
"Paper": "https://papers.nips.cc/paper_files/paper/2023/file/01328d0767830e73a612f9073e9ff15f-Paper-Conference.pdf",
"abstract": "Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis testing problem. The most effective existing attacks estimate the distribution of some test statistic (usually the model's confidence on the true label) on points that were (and were not) used in training by training many \\emph{shadow models}---i.e. models of the same architecture as the model being attacked, trained on a random subsample of data. While effective, these attacks are extremely computationally expensive, especially when the model under attack is large. \\footnotetext[0]{Martin and Shuai are the lead authors, and other authors are ordered alphabetically. {maberlop,shuat}@amazon.com}We introduce a new class of attacks based on performing quantile regression on the distribution of confidence scores induced by the model under attack on points that are not used in training. We show that our method is competitive with state-of-the-art shadow model attacks, while requiring substantially less compute because our attack requires training only a single model. Moreover, unlike shadow model attacks, our proposed attack does not require any knowledge of the architecture of the model under attack and is therefore truly ``black-box\". We show the efficacy of this approach in an extensive series of experiments on various datasets and model architectures. Our code is available at \\href{https://github.com/amazon-science/quantile-mia}{github.com/amazon-science/quantile-mia.}"
}
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