Datasets:

DeepNLP
/

ICLR-2022-Accepted-Papers

title stringlengths 12 151	url stringlengths 41 43	detail_url stringlengths 41 43	authors stringlengths 6 562	tags stringclasses 3 values	abstract stringlengths 519 2.34k	pdf stringlengths 71 71
Domino: Discovering Systematic Errors with Cross-Modal Embeddings	https://openreview.net/forum?id=FPCMqjI0jXN	https://openreview.net/forum?id=FPCMqjI0jXN	Sabri Eyuboglu,Maya Varma,Khaled Kamal Saab,Jean-Benoit Delbrouck,Christopher Lee-Messer,Jared Dunnmon,James Zou,Christopher Re	ICLR 2022,Oral	Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address...	https://openreview.net/pdf/a5ca838a35d810400cfa090453cd85abe02ab6b0.pdf
Natural Language Descriptions of Deep Visual Features	https://openreview.net/forum?id=NudBMY-tzDr	https://openreview.net/forum?id=NudBMY-tzDr	Evan Hernandez,Sarah Schwettmann,David Bau,Teona Bagashvili,Antonio Torralba,Jacob Andreas	ICLR 2022,Oral	Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying neurons that respond to individual concept categories like colors, textures, and object classes. But these techniques are limited in scope,...	https://openreview.net/pdf/842234024e58a8d5073a88b3c04282011b8e20a7.pdf
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization	https://openreview.net/forum?id=tYRrOdSnVUy	https://openreview.net/forum?id=tYRrOdSnVUy	Lixu Wang,Shichao Xu,Ruiqi Xu,Xiao Wang,Qi Zhu	ICLR 2022,Oral	As Artificial Intelligence as a Service gains popularity, protecting well-trained models as intellectual property is becoming increasingly important. There are two common types of protection methods: ownership verification and usage authorization. In this paper, we propose Non-Transferable Learning (NTL), a novel appro...	https://openreview.net/pdf/cc0b829e495ebd36c4e0dcce6f5d044ad4dce58d.pdf
Neural Structured Prediction for Inductive Node Classification	https://openreview.net/forum?id=YWNAX0caEjI	https://openreview.net/forum?id=YWNAX0caEjI	Meng Qu,Huiyu Cai,Jian Tang	ICLR 2022,Oral	This paper studies node classification in the inductive setting, i.e., aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs. This problem has been extensively studied with graph neural networks (GNNs) by learning effective node representations, as well as tr...	https://openreview.net/pdf/df1b628202430dff01a7eeed5b5e5a2e703d1bad.pdf
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"	https://openreview.net/forum?id=uxgg9o7bI_3	https://openreview.net/forum?id=uxgg9o7bI_3	Asiri Wijesinghe,Qing Wang	ICLR 2022,Oral	We propose a new perspective on designing powerful Graph Neural Networks (GNNs). In a nutshell, this enables a general solution to inject structural properties of graphs into a message-passing aggregation scheme of GNNs. As a theoretical basis, we develop a new hierarchy of local isomorphism on neighborhood subgraphs. ...	https://openreview.net/pdf/376e7da3d7f86a2bd40cd51fadfc278e94372443.pdf
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond	https://openreview.net/forum?id=LdlwbBP2mlq	https://openreview.net/forum?id=LdlwbBP2mlq	Chulhee Yun,Shashank Rajput,Suvrit Sra	ICLR 2022,Oral	In distributed learning, local SGD (also known as federated averaging) and its simple baseline minibatch SGD are widely studied optimization methods. Most existing analyses of these methods assume independent and unbiased gradient estimates obtained via with-replacement sampling. In contrast, we study shuffling-based v...	https://openreview.net/pdf/1669f6cc32c853b0d69068b7ed1a230ce3f321d0.pdf
The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions	https://openreview.net/forum?id=Z7Lk2cQEG8a	https://openreview.net/forum?id=Z7Lk2cQEG8a	Yifei Wang,Jonathan Lacotte,Mert Pilanci	ICLR 2022,Oral	We prove that finding all globally optimal two-layer ReLU neural networks can be performed by solving a convex optimization program with cone constraints. Our analysis is novel, characterizes all optimal solutions, and does not leverage duality-based analysis which was recently used to lift neural network training into...	https://openreview.net/pdf/9733b1623c23b45535cc2c126e6fb496e55e8049.pdf
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics	https://openreview.net/forum?id=RQLLzMCefQu	https://openreview.net/forum?id=RQLLzMCefQu	Yonathan Efroni,Dipendra Misra,Akshay Krishnamurthy,Alekh Agarwal,John Langford	ICLR 2022,Oral	Many real-world applications of reinforcement learning (RL) require the agent to deal with high-dimensional observations such as those generated from a megapixel camera. Prior work has addressed such problems with representation learning, through which the agent can provably extract endogenous, latent state information...	https://openreview.net/pdf/310151127bcaaee206f6987dfe48a6f9a49ae848.pdf
Bootstrapped Meta-Learning	https://openreview.net/forum?id=b-ny3x071E5	https://openreview.net/forum?id=b-ny3x071E5	Sebastian Flennerhag,Yannick Schroecker,Tom Zahavy,Hado van Hasselt,David Silver,Satinder Singh	ICLR 2022,Oral	Meta-learning empowers artificial intelligence to increase its efficiency by learning how to learn. Unlocking this potential involves overcoming a challenging meta-optimisation problem. We propose an algorithm that tackles this problem by letting the meta-learner teach itself. The algorithm first bootstraps a target fr...	https://openreview.net/pdf/0eccd48eddcbf9cfc77b50cb0e97fb58937aee70.pdf
Coordination Among Neural Modules Through a Shared Global Workspace	https://openreview.net/forum?id=XzTtHjgPDsT	https://openreview.net/forum?id=XzTtHjgPDsT	Anirudh Goyal,Aniket Rajiv Didolkar,Alex Lamb,Kartikeya Badola,Nan Rosemary Ke,Nasim Rahaman,Jonathan Binas,Charles Blundell,Michael Curtis Mozer,Yoshua Bengio	ICLR 2022,Oral	Deep learning has seen a movement away from representing examples with a monolithic hidden state towards a richly structured state. For example, Transformers segment by position, and object-centric architectures decompose images into entities. In all these architectures, interactions between different elements are mod...	https://openreview.net/pdf/19aac83e8824498df7b9d1e6952523f7c068218b.pdf
Data-Efficient Graph Grammar Learning for Molecular Generation	https://openreview.net/forum?id=l4IHywGq6a	https://openreview.net/forum?id=l4IHywGq6a	Minghao Guo,Veronika Thost,Beichen Li,Payel Das,Jie Chen,Wojciech Matusik	ICLR 2022,Oral	The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples. In practice, however, the size of class-specific chemical datasets is usually limited (e.g., dozens of s...	https://openreview.net/pdf/c17b0db09f98b3279ad677650f18acbf907883ce.pdf
Poisoning and Backdooring Contrastive Learning	https://openreview.net/forum?id=iC4UHbQ01Mp	https://openreview.net/forum?id=iC4UHbQ01Mp	Nicholas Carlini,Andreas Terzis	ICLR 2022,Oral	Multimodal contrastive learning methods like CLIP train on noisy and uncurated training datasets. This is cheaper than labeling datasets manually, and even improves out-of-distribution robustness. We show that this practice makes backdoor and poisoning attacks a significant threat. By poisoning just 0.01% of a dataset ...	https://openreview.net/pdf/abd77f0543a72cd26da355efc5680de233f120af.pdf
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path	https://openreview.net/forum?id=w1UbdvWH_R3	https://openreview.net/forum?id=w1UbdvWH_R3	X.Y. Han,Vardan Papyan,David L. Donoho	ICLR 2022,Oral	The recently discovered Neural Collapse (NC) phenomenon occurs pervasively in today's deep net training paradigm of driving cross-entropy (CE) loss towards zero. During NC, last-layer features collapse to their class-means, both classifiers and class-means collapse to the same Simplex Equiangular Tight Frame, and class...	https://openreview.net/pdf/75799bbe466f7240935655cbfaa930c9628a915e.pdf
Weighted Training for Cross-Task Learning	https://openreview.net/forum?id=ltM1RMZntpu	https://openreview.net/forum?id=ltM1RMZntpu	Shuxiao Chen,Koby Crammer,Hangfeng He,Dan Roth,Weijie J Su	ICLR 2022,Oral	In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks. We show that TAWT is easy to implement, is computationally efficient, requires little hyperparameter tuni...	https://openreview.net/pdf/579ed2f74ecc130396039eae33e13de66b8de08b.pdf
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data	https://openreview.net/forum?id=wRODLDHaAiW	https://openreview.net/forum?id=wRODLDHaAiW	Marine Schimel,Ta-Chu Kao,Kristopher T Jensen,Guillaume Hennequin	ICLR 2022,Oral	Understanding how neural dynamics give rise to behaviour is one of the most fundamental questions in systems neuroscience. To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories can then be related...	https://openreview.net/pdf/c4b2a10a835b79e5cbaff71f6577c29236e964b5.pdf
Extending the WILDS Benchmark for Unsupervised Adaptation	https://openreview.net/forum?id=z7p2V6KROOV	https://openreview.net/forum?id=z7p2V6KROOV	Shiori Sagawa,Pang Wei Koh,Tony Lee,Irena Gao,Sang Michael Xie,Kendrick Shen,Ananya Kumar,Weihua Hu,Michihiro Yasunaga,Henrik Marklund,Sara Beery,Etienne David,Ian Stavness,Wei Guo,Jure Leskovec,Kate Saenko,Tatsunori Hashimoto,Sergey Levine,Chelsea Finn,Percy Liang	ICLR 2022,Oral	Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from ...	https://openreview.net/pdf/16bc69d47c7ff67867bfc50009d6b9fc5043a00f.pdf
Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks	https://openreview.net/forum?id=avgclFZ221l	https://openreview.net/forum?id=avgclFZ221l	S Chandra Mouli,Bruno Ribeiro	ICLR 2022,Oral	Generalizing from observed to new related environments (out-of-distribution) is central to the reliability of classifiers. However, most classifiers fail to predict label $Y$ from input $X$ when the change in environment is due a (stochastic) input transformation $T^\text{te} \circ X'$ not observed in training, as in t...	https://openreview.net/pdf/f15da1dc02ded9aba4a26e8ade750b28429da30f.pdf
Comparing Distributions by Measuring Differences that Affect Decision Making	https://openreview.net/forum?id=KB5onONJIAU	https://openreview.net/forum?id=KB5onONJIAU	Shengjia Zhao,Abhishek Sinha,Yutong He,Aidan Perreault,Jiaming Song,Stefano Ermon	ICLR 2022,Oral	Measuring the discrepancy between two probability distributions is a fundamental problem in machine learning and statistics. We propose a new class of discrepancies based on the optimal loss for a decision task -- two distributions are different if the optimal decision loss is higher on their mixture than on each indiv...	https://openreview.net/pdf/e99719a7a6796b569cc6afdf6f42024d0df2fbea.pdf
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling	https://openreview.net/forum?id=UseMOjWENv	https://openreview.net/forum?id=UseMOjWENv	Yusong Wu,Ethan Manilow,Yi Deng,Rigel Swavely,Kyle Kastner,Tim Cooijmans,Aaron Courville,Cheng-Zhi Anna Huang,Jesse Engel	ICLR 2022,Oral	Musical expression requires control of both what notes that are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for contr...	https://openreview.net/pdf/e26b385d95d67af36d02a385047be6f7a0d6f47b.pdf
Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling	https://openreview.net/forum?id=N0n_QyQ5lBF	https://openreview.net/forum?id=N0n_QyQ5lBF	Bo Wan,Wenjuan Han,Zilong Zheng,Tinne Tuytelaars	ICLR 2022,Oral	We introduce a new task, unsupervised vision-language (VL) grammar induction. Given an image-caption pair, the goal is to extract a shared hierarchical structure for both image and language simultaneously. We argue that such structured output, grounded in both modalities, is a clear step towards the high-level underst...	https://openreview.net/pdf/5c104842d13e8d6efd55b6d7c04f4373a39eae18.pdf
PiCO: Contrastive Label Disambiguation for Partial Label Learning	https://openreview.net/forum?id=EhYjZy6e1gJ	https://openreview.net/forum?id=EhYjZy6e1gJ	Haobo Wang,Ruixuan Xiao,Yixuan Li,Lei Feng,Gang Niu,Gang Chen,Junbo Zhao	ICLR 2022,Oral	Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we b...	https://openreview.net/pdf/f9275b96d741f229db4e61a15ce5f2a499c9ee67.pdf
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting	https://openreview.net/forum?id=0EXmFzUn5I	https://openreview.net/forum?id=0EXmFzUn5I	Shizhan Liu,Hang Yu,Cong Liao,Jianguo Li,Weiyao Lin,Alex X. Liu,Schahram Dustdar	ICLR 2022,Oral	Accurate prediction of the future given the past based on time series data is of paramount importance, since it opens the door for decision making and risk management ahead of time. In practice, the challenge is to build a flexible but parsimonious model that can capture a wide range of temporal dependencies. In this p...	https://openreview.net/pdf/2ac159853cd001bbca6a8a12da497c8013914b31.pdf
Expressiveness and Approximation Properties of Graph Neural Networks	https://openreview.net/forum?id=wIzUeM3TAU	https://openreview.net/forum?id=wIzUeM3TAU	Floris Geerts,Juan L Reutter	ICLR 2022,Oral	Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are, however, usually geared at specific GNNs architectures, and tools for understanding arbitrary GNN architectures are generally lacking. We p...	https://openreview.net/pdf/9d0fe7ff08261aae56611b7f670de9875c2a9cd9.pdf
Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space	https://openreview.net/forum?id=1L0C5ROtFp	https://openreview.net/forum?id=1L0C5ROtFp	Steeven JANNY,Fabien Baradel,Natalia Neverova,Madiha Nadri,Greg Mori,Christian Wolf	ICLR 2022,Oral	Learning causal relationships in high-dimensional data (images, videos) is a hard task, as they are often defined on low dimensional manifolds and must be extracted from complex signals dominated by appearance, lighting, textures and also spurious correlations in the data. We present a method for learning counterfactua...	https://openreview.net/pdf/cbd75b662eaa377753b892113b221d062f26511e.pdf
BEiT: BERT Pre-Training of Image Transformers	https://openreview.net/forum?id=p-BhZSz59o4	https://openreview.net/forum?id=p-BhZSz59o4	Hangbo Bao,Li Dong,Songhao Piao,Furu Wei	ICLR 2022,Oral	We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has two view...	https://openreview.net/pdf/1be2cb0e0edf9af45f8ef450b802b459897cec3d.pdf
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution	https://openreview.net/forum?id=UYneFzXSJWh	https://openreview.net/forum?id=UYneFzXSJWh	Ananya Kumar,Aditi Raghunathan,Robbie Matthew Jones,Tengyu Ma,Percy Liang	ICLR 2022,Oral	When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer---the "head"). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find ...	https://openreview.net/pdf/5d8a4ae4492042b22b07eabc7a9abcfa517f419c.pdf
StyleAlign: Analysis and Applications of Aligned StyleGAN Models	https://openreview.net/forum?id=Qg2vi4ZbHM9	https://openreview.net/forum?id=Qg2vi4ZbHM9	Zongze Wu,Yotam Nitzan,Eli Shechtman,Dani Lischinski	ICLR 2022,Oral	In this paper, we perform an in-depth study of the properties and applications of aligned generative models. We refer to two models as aligned if they share the same architecture, and one of them (the child) is obtained from the other (the parent) via fine-tuning to another domain, a common practice in transfer learnin...	https://openreview.net/pdf/a75f48f49713ac38baaaee51cb3273177975f96b.pdf
Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion	https://openreview.net/forum?id=qnQN4yr6FJz	https://openreview.net/forum?id=qnQN4yr6FJz	Kohei Miyaguchi,Takayuki Katsuki,Akira Koseki,Toshiya Iwamori	ICLR 2022,Oral	We are concerned with the problem of distributional prediction with incomplete features: The goal is to estimate the distribution of target variables given feature vectors with some of the elements missing. A typical approach to this problem is to perform missing-value imputation and regression, simultaneously or seque...	https://openreview.net/pdf/537474668e8264be0d7e7963ad009564621ad25e.pdf
Efficiently Modeling Long Sequences with Structured State Spaces	https://openreview.net/forum?id=uYLFoz1vlAC	https://openreview.net/forum?id=uYLFoz1vlAC	Albert Gu,Karan Goel,Christopher Re	ICLR 2022,Oral	A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they ...	https://openreview.net/pdf/a8eedf494f6698cb467c310c59d3ea6488546805.pdf
Large Language Models Can Be Strong Differentially Private Learners	https://openreview.net/forum?id=bVuP3ltATMz	https://openreview.net/forum?id=bVuP3ltATMz	Xuechen Li,Florian Tramer,Percy Liang,Tatsunori Hashimoto	ICLR 2022,Oral	Differentially Private (DP) learning has seen limited success for building large deep learning models of text, and straightforward attempts at applying Differentially Private Stochastic Gradient Descent (DP-SGD) to NLP tasks have resulted in large performance drops and high computational overhead. We show that this per...	https://openreview.net/pdf/d88e1e721c4085b8a6403837f45b8c483ad0225b.pdf
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation	https://openreview.net/forum?id=PzcvxEMzvQC	https://openreview.net/forum?id=PzcvxEMzvQC	Minkai Xu,Lantao Yu,Yang Song,Chence Shi,Stefano Ermon,Jian Tang	ICLR 2022,Oral	Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep generative models. Inspired by the diffusion process in classical non-equilibrium thermodynamic...	https://openreview.net/pdf/d6be0299d7f2d2bf947d450fffe98c8395458c75.pdf
Frame Averaging for Invariant and Equivariant Network Design	https://openreview.net/forum?id=zIUyj55nXR	https://openreview.net/forum?id=zIUyj55nXR	Omri Puny,Matan Atzmon,Edward J. Smith,Ishan Misra,Aditya Grover,Heli Ben-Hamu,Yaron Lipman	ICLR 2022,Oral	Many machine learning tasks involve learning functions that are known to be invariant or equivariant to certain symmetries of the input data. However, it is often challenging to design neural network architectures that respect these symmetries while being expressive and computationally efficient. For example, Euclidean...	https://openreview.net/pdf/d7849f0ef0f911d06889785dc7116564d5342442.pdf
Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation	https://openreview.net/forum?id=oapKSVM2bcj	https://openreview.net/forum?id=oapKSVM2bcj	Alex Rogozhnikov	ICLR 2022,Oral	Tensor computations underlie modern scientific computing and deep learning. A number of tensor frameworks emerged varying in execution model, hardware support, memory management, model definition, etc. However, tensor operations in all frameworks follow the same paradigm. Recent neural network architectures demonstrate...	https://openreview.net/pdf/d568f6e36eaa377888611b8e0d84076777edc330.pdf
A Fine-Grained Analysis on Distribution Shift	https://openreview.net/forum?id=Dl4LetuLdyK	https://openreview.net/forum?id=Dl4LetuLdyK	Olivia Wiles,Sven Gowal,Florian Stimberg,Sylvestre-Alvise Rebuffi,Ira Ktena,Krishnamurthy Dj Dvijotham,Ali Taylan Cemgil	ICLR 2022,Oral	Robustness to distribution shifts is critical for deploying machine learning models in the real world. Despite this necessity, there has been little work in defining the underlying mechanisms that cause these shifts and evaluating the robustness of algorithms across multiple, different distribution shifts. To this end,...	https://openreview.net/pdf/6be366539738706234ad0b104ed82361a3c5f6e7.pdf
Open-Set Recognition: A Good Closed-Set Classifier is All You Need	https://openreview.net/forum?id=5hLP5JY9S2d	https://openreview.net/forum?id=5hLP5JY9S2d	Sagar Vaze,Kai Han,Andrea Vedaldi,Andrew Zisserman	ICLR 2022,Oral	The ability to identify whether or not a test sample belongs to one of the semantic classes in a classifier's training set is critical to practical deployment of the model. This task is termed open-set recognition (OSR) and has received significant attention in recent years. In this paper, we first demonstrate that the...	https://openreview.net/pdf/a9e422d293a936fe65575b5e1ea6a86549b84bca.pdf
Learning Strides in Convolutional Neural Networks	https://openreview.net/forum?id=M752z9FKJP	https://openreview.net/forum?id=M752z9FKJP	Rachid Riad,Olivier Teboul,David Grangier,Neil Zeghidour	ICLR 2022,Oral	Convolutional neural networks typically contain several downsampling operators, such as strided convolutions or pooling layers, that progressively reduce the resolution of intermediate representations. This provides some shift-invariance while reducing the computational complexity of the whole architecture. A critical ...	https://openreview.net/pdf/1bc01ea49b5a288387ac5de300847b1d6690d940.pdf
Understanding over-squashing and bottlenecks on graphs via curvature	https://openreview.net/forum?id=7UmjRGzp-A	https://openreview.net/forum?id=7UmjRGzp-A	Jake Topping,Francesco Di Giovanni,Benjamin Paul Chamberlain,Xiaowen Dong,Michael M. Bronstein	ICLR 2022,Oral	Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph. Recent works pointed to the distortion of information flowing from distant nodes as a factor limiting the efficiency of message passing for tasks relying on long-distance interactions. This phen...	https://openreview.net/pdf/f6b974eac8792a0d8d59633044276dabbf9d01c9.pdf
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme	https://openreview.net/forum?id=8c50f-DoWAu	https://openreview.net/forum?id=8c50f-DoWAu	Vadim Popov,Ivan Vovk,Vladimir Gogoryan,Tasnima Sadekova,Mikhail Sergeevich Kudinov,Jiansheng Wei	ICLR 2022,Oral	Voice conversion is a common speech synthesis task which can be solved in different ways depending on a particular real-world scenario. The most challenging one often referred to as one-shot many-to-many voice conversion consists in copying target voice from only one reference utterance in the most general case when bo...	https://openreview.net/pdf/468145b46e459c5ba69e7017b6ef4eaece277e94.pdf
Resolving Training Biases via Influence-based Data Relabeling	https://openreview.net/forum?id=EskfH0bwNVn	https://openreview.net/forum?id=EskfH0bwNVn	Shuming Kong,Yanyan Shen,Linpeng Huang	ICLR 2022,Oral	The performance of supervised learning methods easily suffers from the training bias issue caused by train-test distribution mismatch or label noise. Influence function is a technique that estimates the impacts of a training sample on the model’s predictions. Recent studies on \emph{data resampling} have employed infl...	https://openreview.net/pdf/64c51657be7bb5a9efecafe39344c719ccb4d394.pdf
Representational Continuity for Unsupervised Continual Learning	https://openreview.net/forum?id=9Hrka5PA7LW	https://openreview.net/forum?id=9Hrka5PA7LW	Divyam Madaan,Jaehong Yoon,Yuanchun Li,Yunxin Liu,Sung Ju Hwang	ICLR 2022,Oral	Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual learning (SCL) scenarios. Consequently, they are not scalable to real-world applications where the data distribution is often biased and unan...	https://openreview.net/pdf/947f2c6dc3cd63a83d402bf9cbaddf42e362709e.pdf
Vision-Based Manipulators Need to Also See from Their Hands	https://openreview.net/forum?id=RJkAHKp7kNZ	https://openreview.net/forum?id=RJkAHKp7kNZ	Kyle Hsu,Moo Jin Kim,Rafael Rafailov,Jiajun Wu,Chelsea Finn	ICLR 2022,Oral	We study how the choice of visual perspective affects learning and generalization in the context of physical manipulation from raw sensor observations. Compared with the more commonly used global third-person perspective, a hand-centric (eye-in-hand) perspective affords reduced observability, but we find that it consis...	https://openreview.net/pdf/bf5308ad68220347e7cbf2dcbedbf7bb4e0a21b1.pdf
Meta-Learning with Fewer Tasks through Task Interpolation	https://openreview.net/forum?id=ajXWF7bVR8d	https://openreview.net/forum?id=ajXWF7bVR8d	Huaxiu Yao,Linjun Zhang,Chelsea Finn	ICLR 2022,Oral	Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge. However, the bottleneck of current meta-learning algorithms is the requirement of a large number of meta-training tasks, which may not be accessible in real-world sce...	https://openreview.net/pdf/ebbfc5841da414394c96beeba92500546061461a.pdf
DISCOVERING AND EXPLAINING THE REPRESENTATION BOTTLENECK OF DNNS	https://openreview.net/forum?id=iRCUlgmdfHJ	https://openreview.net/forum?id=iRCUlgmdfHJ	Huiqi Deng,Qihan Ren,Hao Zhang,Quanshi Zhang	ICLR 2022,Oral	This paper explores the bottleneck of feature representations of deep neural networks (DNNs), from the perspective of the complexity of interactions between input variables encoded in DNNs. To this end, we focus on the multi-order interaction between input variables, where the order represents the complexity of interac...	https://openreview.net/pdf/e470657e4d47a20411713a973ed0282f87c9f9a9.pdf
Sparse Communication via Mixed Distributions	https://openreview.net/forum?id=WAid50QschI	https://openreview.net/forum?id=WAid50QschI	António Farinhas,Wilker Aziz,Vlad Niculae,Andre Martins	ICLR 2022,Oral	Neural networks and other machine learning models compute continuous representations, while humans communicate mostly through discrete symbols. Reconciling these two forms of communication is desirable for generating human-readable interpretations or learning discrete latent variable models, while maintaining end-to-en...	https://openreview.net/pdf/f8c966f98befffb0bfbd9af921a4e4dd831d549f.pdf
Finetuned Language Models are Zero-Shot Learners	https://openreview.net/forum?id=gEZrGCozdqR	https://openreview.net/forum?id=gEZrGCozdqR	Jason Wei,Maarten Bosma,Vincent Zhao,Kelvin Guu,Adams Wei Yu,Brian Lester,Nan Du,Andrew M. Dai,Quoc V Le	ICLR 2022,Oral	This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning—finetuning language models on a collection of datasets described via instructions—substantially improves zero-shot performance on unseen tasks. We take a 137B parameter pretrained langu...	https://openreview.net/pdf/16b50405ab1e3ac1e2f76190ee62a48c496c568d.pdf
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization	https://openreview.net/forum?id=_CfpJazzXT2	https://openreview.net/forum?id=_CfpJazzXT2	Qing Jin,Jian Ren,Richard Zhuang,Sumant Hanumante,Zhengang Li,Zhiyu Chen,Yanzhi Wang,Kaiyuan Yang,Sergey Tulyakov	ICLR 2022,Oral	Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there is a performance gap between quantized and full-precision models. To reduce it, existing quantization approaches require high-precision INT3...	https://openreview.net/pdf/aed69dd0c10990a2c4948e6d230de04c5719fb7d.pdf
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design	https://openreview.net/forum?id=UcDUxjPYWSr	https://openreview.net/forum?id=UcDUxjPYWSr	Ye Yuan,Yuda Song,Zhengyi Luo,Wen Sun,Kris M. Kitani	ICLR 2022,Oral	An agent's functionality is largely determined by its design, i.e., skeletal structure and joint attributes (e.g., length, size, strength). However, finding the optimal agent design for a given function is extremely challenging since the problem is inherently combinatorial and the design space is prohibitively large. A...	https://openreview.net/pdf/511a5c95afacad18125605721a8d1e530c07018b.pdf
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics	https://openreview.net/forum?id=s03AQxehtd_	https://openreview.net/forum?id=s03AQxehtd_	Boris N. Oreshkin,Florent Bocquelet,Felix G. Harvey,Bay Raitt,Dominic Laflamme	ICLR 2022,Oral	Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling. Specifically, we tackle the problem of constructing a full static human pose based on sparse and variable user inputs (e.g. locations and/or orientations of a subset of body joints). To sol...	https://openreview.net/pdf/72eadcfe21558f0be18ff071adc50adc3ae85e5e.pdf
Hyperparameter Tuning with Renyi Differential Privacy	https://openreview.net/forum?id=-70L8lpp9DF	https://openreview.net/forum?id=-70L8lpp9DF	Nicolas Papernot,Thomas Steinke	ICLR 2022,Oral	For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy leakage resulting from the multiple training runs needed to fin...	https://openreview.net/pdf/8832d0e112b9fd6c5c8f0be8a093625e4de6e337.pdf
Real-Time Neural Voice Camouflage	https://openreview.net/forum?id=qj1IZ-6TInc	https://openreview.net/forum?id=qj1IZ-6TInc	Mia Chiquier,Chengzhi Mao,Carl Vondrick	ICLR 2022,Oral	Automatic speech recognition systems have created exciting possibilities for applications, however they also enable opportunities for systematic eavesdropping.We propose a method to camouflage a person's voice from these systems without inconveniencing the conversation between people in the room. Standard adversarial a...	https://openreview.net/pdf/e2b96a38db73636bfa51d5ee4097373ddda15329.pdf
CycleMLP: A MLP-like Architecture for Dense Prediction	https://openreview.net/forum?id=NMEceG4v69Y	https://openreview.net/forum?id=NMEceG4v69Y	Shoufa Chen,Enze Xie,Chongjian GE,Runjian Chen,Ding Liang,Ping Luo	ICLR 2022,Oral	This paper presents a simple MLP-like architecture, CycleMLP, which is a versatile backbone for visual recognition and dense predictions. As compared to modern MLP architectures, e.g. , MLP-Mixer, ResMLP, and gMLP, whose architectures are correlated to image size and thus are infeasible in object detection and segmenta...	https://openreview.net/pdf/0ff0f728cbc430b36ea84288793e887e216cff59.pdf
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models	https://openreview.net/forum?id=0xiJLKH-ufZ	https://openreview.net/forum?id=0xiJLKH-ufZ	Fan Bao,Chongxuan Li,Jun Zhu,Bo Zhang	ICLR 2022,Oral	Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate over thousands of timesteps. A key problem in the inference is to estimate the variance in each timestep of the reverse process. In this wo...	https://openreview.net/pdf/541cdc9e000367bb0bd3fc42201573ed434094c8.pdf
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation	https://openreview.net/forum?id=uSE03demja	https://openreview.net/forum?id=uSE03demja	Pingchuan Ma,Tao Du,Joshua B. Tenenbaum,Wojciech Matusik,Chuang Gan	ICLR 2022,Oral	This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible. Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries doma...	https://openreview.net/pdf/999353870633727a2d50bc5b4ee873b50401eba7.pdf
The Information Geometry of Unsupervised Reinforcement Learning	https://openreview.net/forum?id=3wU2UX0voE	https://openreview.net/forum?id=3wU2UX0voE	Benjamin Eysenbach,Ruslan Salakhutdinov,Sergey Levine	ICLR 2022,Oral	How can a reinforcement learning (RL) agent prepare to solve downstream tasks if those tasks are not known a priori? One approach is unsupervised skill discovery, a class of algorithms that learn a set of policies without access to a reward function. Such algorithms bear a close resemblance to representation learning a...	https://openreview.net/pdf/4709236cdf10497a057511e94fe99f87770c5bf6.pdf
Language modeling via stochastic processes	https://openreview.net/forum?id=pMQwKL1yctf	https://openreview.net/forum?id=pMQwKL1yctf	Rose E Wang,Esin Durmus,Noah Goodman,Tatsunori Hashimoto	ICLR 2022,Oral	Modern language models can generate high-quality short texts. However, they often meander or are incoherent when generating longer texts. These issues arise from the next-token-only language modeling objective. To address these issues, we introduce Time Control (TC), a language model that implicitly plans via a latent ...	https://openreview.net/pdf/ceeec650a60b1f87ad4dda26ecd02c9df0e3ed9d.pdf
Learning to Downsample for Segmentation of Ultra-High Resolution Images	https://openreview.net/forum?id=HndgQudNb91	https://openreview.net/forum?id=HndgQudNb91	Chen Jin,Ryutaro Tanno,Thomy Mertzanidou,Eleftheria Panagiotaki,Daniel C. Alexander	ICLR 2022,Poster	Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work,...	https://openreview.net/pdf/d2ade7120315e0521c4b97b593c4a2ebd44b0652.pdf
Variational Neural Cellular Automata	https://openreview.net/forum?id=7fFO4cMBx_9	https://openreview.net/forum?id=7fFO4cMBx_9	Rasmus Berg Palm,Miguel González Duque,Shyam Sudhakaran,Sebastian Risi	ICLR 2022,Poster	In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms --- algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process. Inspired by the incredible diversity of this biological generative process, we propose a generative model...	https://openreview.net/pdf/abec641c2a0c18536da3345e5cd92d673d90b69d.pdf
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation	https://openreview.net/forum?id=FKp8-pIRo3y	https://openreview.net/forum?id=FKp8-pIRo3y	Todor Davchev,Oleg Olegovich Sushkov,Jean-Baptiste Regli,Stefan Schaal,Yusuf Aytar,Markus Wulfmeier,Jon Scholz	ICLR 2022,Poster	Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of ``narrow passages'' in their state space. Solving such tasks with a sparse reward in a sample-efficient manner poses a challenge to modern reinforcement learning (RL) due to the associated long-horizon nature ...	https://openreview.net/pdf/524d4c3cacc5ff7803cd7061b33991511fee7db7.pdf
L0-Sparse Canonical Correlation Analysis	https://openreview.net/forum?id=KntaNRo6R48	https://openreview.net/forum?id=KntaNRo6R48	Ofir Lindenbaum,Moshe Salhov,Amir Averbuch,Yuval Kluger	ICLR 2022,Poster	Canonical Correlation Analysis (CCA) models are powerful for studying the associations between two sets of variables. The canonically correlated representations, termed \textit{canonical variates} are widely used in unsupervised learning to analyze unlabeled multi-modal registered datasets. Despite their success, CCA m...	https://openreview.net/pdf/69ae8c04ac43812f7523f009313daec68f09ea3d.pdf
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?	https://openreview.net/forum?id=B7ZbqNLDn-_	https://openreview.net/forum?id=B7ZbqNLDn-_	Sheikh Shams Azam,Seyyedali Hosseinalipour,Qiang Qiu,Christopher Brinton	ICLR 2022,Poster	In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gradients (i.e., the gradient-space) in centralized model training, and observe that the gradient-spac...	https://openreview.net/pdf/76e2433c08e957e7f19a49e6815d0f6b52da92cd.pdf
Is Homophily a Necessity for Graph Neural Networks?	https://openreview.net/forum?id=ucASPPD9GKN	https://openreview.net/forum?id=ucASPPD9GKN	Yao Ma,Xiaorui Liu,Neil Shah,Jiliang Tang	ICLR 2022,Poster	Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node classification, GNNs are widely believed to work well due to the homophily assumption (``like attracts like''), and fail to generalize to hete...	https://openreview.net/pdf/dba6b2a528efebfb036a0b908ecfc59201204429.pdf
DEGREE: Decomposition Based Explanation for Graph Neural Networks	https://openreview.net/forum?id=Ve0Wth3ptT_	https://openreview.net/forum?id=Ve0Wth3ptT_	Qizhang Feng,Ninghao Liu,Fan Yang,Ruixiang Tang,Mengnan Du,Xia Hu	ICLR 2022,Poster	Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fall into approximatio...	https://openreview.net/pdf/fd7de8640028480fa9fe56dd9ed7bcad9182bf31.pdf
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds	https://openreview.net/forum?id=T0B9AoM_bFg	https://openreview.net/forum?id=T0B9AoM_bFg	Rob Brekelmans,Sicong Huang,Marzyeh Ghassemi,Greg Ver Steeg,Roger Baker Grosse,Alireza Makhzani	ICLR 2022,Poster	Mutual information (MI) is a fundamental quantity in information theory and machine learning. However, direct estimation of MI is intractable, even if the true joint probability density for the variables of interest is known, as it involves estimating a potentially high-dimensional log partition function. In this work,...	https://openreview.net/pdf/a68f8e4bbad21f5599f372c94827c5f596c6555b.pdf
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods	https://openreview.net/forum?id=bp-LJ4y_XC	https://openreview.net/forum?id=bp-LJ4y_XC	Xueyuan She,Saurabh Dash,Saibal Mukhopadhyay	ICLR 2022,Poster	A dynamical system of spiking neurons with only feedforward connections can classify spatiotemporal patterns without recurrent connections. However, the theoretical construct of a feedforward spiking neural network (SNN) for approximating a temporal sequence remains unclear, making it challenging to optimize SNN archit...	https://openreview.net/pdf/043f00a3e618d0c71bbd79dffbdfdaf6d9fd4d1b.pdf
Diverse Client Selection for Federated Learning via Submodular Maximization	https://openreview.net/forum?id=nwKXyFvaUm	https://openreview.net/forum?id=nwKXyFvaUm	Ravikumar Balakrishnan,Tian Li,Tianyi Zhou,Nageen Himayat,Virginia Smith,Jeff Bilmes	ICLR 2022,Poster	In every communication round of federated learning, a random subset of clients communicate their model updates back to the server which then aggregates them all. The optimal size of this subset is not known and several studies have shown that typically random selection does not perform very well in terms of ...	https://openreview.net/pdf/4d539789e55d133a96781cda576be4ab34ec5982.pdf
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation	https://openreview.net/forum?id=jT1EwXu-4hj	https://openreview.net/forum?id=jT1EwXu-4hj	Da Xu,Yuting Ye,Chuanwei Ruan,Evren Korpeoglu,Sushant Kumar,Kannan Achan	ICLR 2022,Poster	The interventional nature of recommendation has attracted increasing attention in recent years. It particularly motivates researchers to formulate learning and evaluating recommendation as causal inference and data missing-not-at-random problems. However, few take seriously the consequence of violating the critical ass...	https://openreview.net/pdf/22322b458fd437ff0b3cf13debd29cc381b25ccc.pdf
Variational Predictive Routing with Nested Subjective Timescales	https://openreview.net/forum?id=JxFgJbZ-wft	https://openreview.net/forum?id=JxFgJbZ-wft	Alexey Zakharov,Qinghai Guo,Zafeirios Fountas	ICLR 2022,Poster	Discovery and learning of an underlying spatiotemporal hierarchy in sequential data is an important topic for machine learning. Despite this, little work has been done to explore hierarchical generative models that can flexibly adapt their layerwise representations in response to datasets with different temporal dynami...	https://openreview.net/pdf/712c74938a55973dd0b3f46e154fc0696194b578.pdf
Sample and Computation Redistribution for Efficient Face Detection	https://openreview.net/forum?id=RhB1AdoFfGE	https://openreview.net/forum?id=RhB1AdoFfGE	Jia Guo,Jiankang Deng,Alexandros Lattas,Stefanos Zafeiriou	ICLR 2022,Poster	Although tremendous strides have been made in uncontrolled face detection, accurate face detection with a low computation cost remains an open challenge. In this paper, we point out that computation distribution and scale augmentation are the keys to detecting small faces from low-resolution images. Motivated by these ...	https://openreview.net/pdf/d7b9dd38011f418b1c66bb378aef38a25d8c9bf5.pdf
Sound Adversarial Audio-Visual Navigation	https://openreview.net/forum?id=NkZq4OEYN-	https://openreview.net/forum?id=NkZq4OEYN-	Yinfeng Yu,Wenbing Huang,Fuchun Sun,Changan Chen,Yikai Wang,Xiaohong Liu	ICLR 2022,Poster	Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visual navigation works assume a clean environment that solely contains the target sound, which, however, would not be suitable in most real-wor...	https://openreview.net/pdf/892cdd541646cc28a0880494951fbd89079c2a3d.pdf
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations	https://openreview.net/forum?id=12RoR2o32T	https://openreview.net/forum?id=12RoR2o32T	Aahlad Manas Puli,Lily H Zhang,Eric Karl Oermann,Rajesh Ranganath	ICLR 2022,Poster	In many prediction problems, spurious correlations are induced by a changing relationship between the label and a nuisance variable that is also correlated with the covariates. For example, in classifying animals in natural images, the background, which is a nuisance, can predict the type of animal. This nuisance-label...	https://openreview.net/pdf/7128d52f12e20439db2d07083f3de3995967bb53.pdf
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis	https://openreview.net/forum?id=OM_lYiHXiCL	https://openreview.net/forum?id=OM_lYiHXiCL	Junfeng Guo,Ang Li,Cong Liu	ICLR 2022,Poster	Deep neural networks (DNNs) are proved to be vulnerable against backdoor attacks. A backdoor could be embedded in the target DNNs through injecting a backdoor trigger into the training examples, which can cause the target DNNs misclassify an input attached with the backdoor trigger. Recent backdoor detection methods o...	https://openreview.net/pdf/b8ad85b4ddd615a5abac4d7c1d5713fc92b9f0e9.pdf
Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum	https://openreview.net/forum?id=5ECQL05ub0J	https://openreview.net/forum?id=5ECQL05ub0J	Kirby Banman,Garnet Liam Peet-Pare,Nidhi Hegde,Alona Fyshe,Martha White	ICLR 2022,Poster	Most convergence guarantees for stochastic gradient descent with momentum (SGDm) rely on iid sampling. Yet, SGDm is often used outside this regime, in settings with temporally correlated input samples such as continual learning and reinforcement learning. Existing work has shown that SGDm with a decaying step-size can...	https://openreview.net/pdf/967691b8c1cb517500d87dfd7dbf7dd6293c0e89.pdf
Top-label calibration and multiclass-to-binary reductions	https://openreview.net/forum?id=WqoBaaPHS-	https://openreview.net/forum?id=WqoBaaPHS-	Chirag Gupta,Aaditya Ramdas	ICLR 2022,Poster	We propose a new notion of multiclass calibration called top-label calibration. A classifier is said to be top-label calibrated if the reported probability for the predicted class label---the top-label---is calibrated, conditioned on the top-label. This conditioning is essential for practical utility of the calibration...	https://openreview.net/pdf/a580ad8d84d1a31adcccb9f9e2102c3b503121df.pdf
Anisotropic Random Feature Regression in High Dimensions	https://openreview.net/forum?id=JfaWawZ8BmX	https://openreview.net/forum?id=JfaWawZ8BmX	Gabriel Mel,Jeffrey Pennington	ICLR 2022,Poster	In contrast to standard statistical wisdom, modern learning algorithms typically find their best performance in the overparameterized regime in which the model has many more parameters than needed to fit the training data. A growing number of recent works have shown that random feature models can offer a detailed theor...	https://openreview.net/pdf/bc2ddad146bd93609c8510aac28ae824072d1832.pdf
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future	https://openreview.net/forum?id=L01Nn_VJ9i	https://openreview.net/forum?id=L01Nn_VJ9i	Harshavardhan Kamarthi,Alexander Rodríguez,B. Aditya Prakash	ICLR 2022,Poster	For real-time forecasting in domains like public health and macroeconomics, data collection is a non-trivial and demanding task. Often after being initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches a stable value...	https://openreview.net/pdf/5ff5a41a0773c6764d009a86a74cce3dd35e8ec3.pdf
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective	https://openreview.net/forum?id=lrocYB-0ST2	https://openreview.net/forum?id=lrocYB-0ST2	Alberto Bietti	ICLR 2022,Poster	The empirical success of deep convolutional networks on tasks involving high-dimensional data such as images or audio suggests that they can efficiently approximate certain functions that are well-suited for such tasks. In this paper, we study this through the lens of kernel methods, by considering simple hierarchical ...	https://openreview.net/pdf/35eeb8c9531f39eb14e07db8fb296d38b7f1a369.pdf
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning	https://openreview.net/forum?id=vgqS1vkkCbE	https://openreview.net/forum?id=vgqS1vkkCbE	Dhruv Shah,Peng Xu,Yao Lu,Ted Xiao,Alexander T Toshev,Sergey Levine,brian ichter	ICLR 2022,Poster	Reinforcement learning can train policies that effectively perform complex tasks. However for long-horizon tasks, the performance of these methods degrades with horizon, often necessitating reasoning over and chaining lower-level skills. Hierarchical reinforcement learning aims to enable this by providing a bank of low...	https://openreview.net/pdf/c49d03d6fc757e37898cc5399159de2e30589146.pdf
Fast Regression for Structured Inputs	https://openreview.net/forum?id=gNp54NxHUPJ	https://openreview.net/forum?id=gNp54NxHUPJ	Raphael A Meyer,Cameron N Musco,Christopher P Musco,David Woodruff,Samson Zhou	ICLR 2022,Poster	We study the $\ell_p$ regression problem, which requires finding $\mathbf{x}\in\mathbb R^{d}$ that minimizes $\\|\mathbf{A}\mathbf{x}-\mathbf{b}\\|_p$ for a matrix $\mathbf{A}\in\mathbb R^{n \times d}$ and response vector $\mathbf{b}\in\mathbb R^{n}$. There has been recent interest in developing subsampling methods for t...	https://openreview.net/pdf/a76864e8c343a5dcb3414cc8caa6fc2fdd2afc19.pdf
CrossBeam: Learning to Search in Bottom-Up Program Synthesis	https://openreview.net/forum?id=qhC8mr2LEKq	https://openreview.net/forum?id=qhC8mr2LEKq	Kensen Shi,Hanjun Dai,Kevin Ellis,Charles Sutton	ICLR 2022,Poster	Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to guide combinatorial search algorithms, but such approaches still explore a huge portion of the search space and quickly become intractable a...	https://openreview.net/pdf/d098dde7689c9940303ddd8c11f5f44e8b866692.pdf
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning	https://openreview.net/forum?id=M6M8BEmd6dq	https://openreview.net/forum?id=M6M8BEmd6dq	Seng Pei Liew,Tsubasa Takahashi,Michihiko Ueno	ICLR 2022,Poster	We propose a new framework of synthesizing data using deep generative models in a differentially private manner. Within our framework, sensitive data are sanitized with rigorous privacy guarantees in a one-shot fashion, such that training deep generative models is possible without re-using the original data. Hence, no ...	https://openreview.net/pdf/3efedef6ce8396ae22861cd7154606c25bd31e95.pdf
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet	https://openreview.net/forum?id=aOX3a9q3RVV	https://openreview.net/forum?id=aOX3a9q3RVV	Michelle Miller,SueYeon Chung,Kenneth D. Miller	ICLR 2022,Poster	Local divisive normalization provides a phenomenological description of many nonlinear response properties of neurons across visual cortical areas. To gain insight into the utility of this operation, we studied the effects on AlexNet of a local divisive normalization between features, with learned parameters. Developin...	https://openreview.net/pdf/452011d69839dd4fa39ba4bec882b24cb5bb2649.pdf
Evaluating Distributional Distortion in Neural Language Modeling	https://openreview.net/forum?id=bTteFbU99ye	https://openreview.net/forum?id=bTteFbU99ye	Benjamin LeBrun,Alessandro Sordoni,Timothy J. O'Donnell	ICLR 2022,Poster	A fundamental characteristic of natural language is the high rate at which speakers produce novel expressions. Because of this novelty, a heavy-tail of rare events accounts for a significant amount of the total probability mass of distributions in language (Baayen, 2001). Standard language modeling metrics such as perp...	https://openreview.net/pdf/c22ea9d1df97b96c390eb350b4c09eb8e2388128.pdf
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining	https://openreview.net/forum?id=r5qumLiYwf9	https://openreview.net/forum?id=r5qumLiYwf9	Ahmed Imtiaz Humayun,Randall Balestriero,Richard Baraniuk	ICLR 2022,Poster	Deep Generative Networks (DGNs) are extensively employed in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and their variants to approximate the data manifold, and data distribution on that manifold. However, training samples are often obtained based on preferences, costs, or convenience produ...	https://openreview.net/pdf/e9c0ccdf7ecc11a5666ac100d75f89816ce7c0f7.pdf
Neural Contextual Bandits with Deep Representation and Shallow Exploration	https://openreview.net/forum?id=xnYACQquaGV	https://openreview.net/forum?id=xnYACQquaGV	Pan Xu,Zheng Wen,Handong Zhao,Quanquan Gu	ICLR 2022,Poster	We study neural contextual bandits, a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the specific reward generating function is unknown. We propose a novel learning algorithm that transforms the raw feature vector using the last hidden layer of a deep Re...	https://openreview.net/pdf/c6ee94e7fd22670895280aaf06535b6373d428eb.pdf
PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks	https://openreview.net/forum?id=NoB8YgRuoFU	https://openreview.net/forum?id=NoB8YgRuoFU	Siyan Liu,Pei Zhang,Dan Lu,Guannan Zhang	ICLR 2022,Poster	We propose a novel prediction interval (PI) method for uncertainty quantification, which addresses three major issues with the state-of-the-art PI methods. First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple P...	https://openreview.net/pdf/84a3741f26e65df3c7b232779bcfb5dac283d41e.pdf
Discriminative Similarity for Data Clustering	https://openreview.net/forum?id=kj0_45Y4r9i	https://openreview.net/forum?id=kj0_45Y4r9i	Yingzhen Yang,Ping Li	ICLR 2022,Poster	Similarity-based clustering methods separate data into clusters according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose {\em Clustering by Discriminative Similarity (CDS)}, a novel method which learns discriminative similarity for d...	https://openreview.net/pdf/b159fb24355dd1bf64f74a757973bbc8cc96d57e.pdf
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation	https://openreview.net/forum?id=q4tZR1Y-UIs	https://openreview.net/forum?id=q4tZR1Y-UIs	Yuqing Du,Pieter Abbeel,Aditya Grover	ICLR 2022,Poster	We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals. Training such agents efficiently requires automatic generation of a goal curriculum. This is challenging as it requires (a) exploring goals of increasing difficulty, while ensuring that the agent (b) is e...	https://openreview.net/pdf/68a6237e79699c723ce9c9c39537422391df3e2b.pdf
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing	https://openreview.net/forum?id=HOjLHrlZhmx	https://openreview.net/forum?id=HOjLHrlZhmx	Fan Wu,Linyi Li,Zijian Huang,Yevgeniy Vorobeychik,Ding Zhao,Bo Li	ICLR 2022,Poster	As reinforcement learning (RL) has achieved great success and been even adopted in safety-critical domains such as autonomous vehicles, a range of empirical studies have been conducted to improve its robustness against adversarial attacks. However, how to certify its robustness with theoretical guarantees still remains...	https://openreview.net/pdf/b79f87ced196c2a5a13ca10bae3d39a8924b08b8.pdf
Neural Link Prediction with Walk Pooling	https://openreview.net/forum?id=CCu6RcUMwK0	https://openreview.net/forum?id=CCu6RcUMwK0	Liming Pan,Cheng Shi,Ivan Dokmanić	ICLR 2022,Poster	Graph neural networks achieve high accuracy in link prediction by jointly leveraging graph topology and node attributes. Topology, however, is represented indirectly; state-of-the-art methods based on subgraph classification label nodes with distance to the target link, so that, although topological information is pres...	https://openreview.net/pdf/ad031c5e836c55357e2f13cdb18fa502a7eecc80.pdf
On the Convergence of Certified Robust Training with Interval Bound Propagation	https://openreview.net/forum?id=YeShU5mLfLt	https://openreview.net/forum?id=YeShU5mLfLt	Yihan Wang,Zhouxing Shi,Quanquan Gu,Cho-Jui Hsieh	ICLR 2022,Poster	Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial perturbations present, while the convergence of IBP training remains unknown in existing literature. In this paper, we present a theoretical anal...	https://openreview.net/pdf/4e7f7f34a6f11b062e283b3a04324bb373e39067.pdf
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators	https://openreview.net/forum?id=sX3XaHwotOg	https://openreview.net/forum?id=sX3XaHwotOg	Yu Meng,Chenyan Xiong,Payal Bajaj,saurabh tiwary,Paul N. Bennett,Jiawei Han,Xia Song	ICLR 2022,Poster	We present a new framework AMOS that pretrains text encoders with an Adversarial learning curriculum via a Mixture Of Signals from multiple auxiliary generators. Following ELECTRA-style pretraining, the main encoder is trained as a discriminator to detect replaced tokens generated by auxiliary masked language models (M...	https://openreview.net/pdf/4127a755f1e5ee998e6423f7a8d734f9e88b8cab.pdf
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations	https://openreview.net/forum?id=0jP2n0YFmKG	https://openreview.net/forum?id=0jP2n0YFmKG	Anuroop Sriram,Abhishek Das,Brandon M Wood,Siddharth Goyal,C. Lawrence Zitnick	ICLR 2022,Poster	Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change. However, the GNNs that have proven most effective for this task are memory inten...	https://openreview.net/pdf/d00345679f2290baeabb225428516fad14fea79e.pdf
Understanding and Leveraging Overparameterization in Recursive Value Estimation	https://openreview.net/forum?id=shbAgEsk3qM	https://openreview.net/forum?id=shbAgEsk3qM	Chenjun Xiao,Bo Dai,Jincheng Mei,Oscar A Ramirez,Ramki Gummadi,Chris Harris,Dale Schuurmans	ICLR 2022,Poster	The theory of function approximation in reinforcement learning (RL) typically considers low capacity representations that incur a tradeoff between approximation error, stability and generalization. Current deep architectures, however, operate in an overparameterized regime where approximation error is not necessarily ...	https://openreview.net/pdf/c5131ad5930c1a9f32ede673f284175158a75792.pdf
Optimization and Adaptive Generalization of Three layer Neural Networks	https://openreview.net/forum?id=dPyRNUlttBv	https://openreview.net/forum?id=dPyRNUlttBv	Khashayar Gatmiry,Stefanie Jegelka,Jonathan Kelner	ICLR 2022,Poster	While there has been substantial recent work studying generalization of neural networks, the ability of deep nets in automating the process of feature extraction still evades a thorough mathematical understanding. As a step toward this goal, we analyze learning and generalization of a three-layer neural network wit...	https://openreview.net/pdf/086ce10c9607a92d59635b0ac0f1f0bd8c86ae5b.pdf
Non-Parallel Text Style Transfer with Self-Parallel Supervision	https://openreview.net/forum?id=-TSe5o7STVR	https://openreview.net/forum?id=-TSe5o7STVR	Ruibo Liu,Chongyang Gao,Chenyan Jia,Guangxuan Xu,Soroush Vosoughi	ICLR 2022,Poster	The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping exists between sentences of the source and target style; the style transfer models thus only receive weak supervision of the target sentenc...	https://openreview.net/pdf/7858e341aa92c11991455a43e9a78c35ee4655a2.pdf
Can an Image Classifier Suffice For Action Recognition?	https://openreview.net/forum?id=qhkFX-HLuHV	https://openreview.net/forum?id=qhkFX-HLuHV	Quanfu Fan,Chun-Fu Chen,Rameswar Panda	ICLR 2022,Poster	We explore a new perspective on video understanding by casting the video recognition problem as an image recognition task. Our approach rearranges input video frames into super images, which allow for training an image classifier directly to fulfill the task of action recognition, in exactly the same way as image class...	https://openreview.net/pdf/30716aa30d9fbd5e0f9a95e4c0e1255607ab8bc4.pdf
Interacting Contour Stochastic Gradient Langevin Dynamics	https://openreview.net/forum?id=IK9ap6nxXr2	https://openreview.net/forum?id=IK9ap6nxXr2	Wei Deng,Siqi Liang,Botao Hao,Guang Lin,Faming Liang	ICLR 2022,Poster	We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD can be theoretically more efficient than a single-chain CSGLD with an equival...	https://openreview.net/pdf/bf454b672f7afe0c72e3a83029c7238309a1b4a0.pdf
NeuPL: Neural Population Learning	https://openreview.net/forum?id=MIX3fJkl_1	https://openreview.net/forum?id=MIX3fJkl_1	Siqi Liu,Luke Marris,Daniel Hennes,Josh Merel,Nicolas Heess,Thore Graepel	ICLR 2022,Poster	Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, growing a policy population that is robust to exploit. This iterative approach suffers from two issues in real-world games: a) under finite bu...	https://openreview.net/pdf/eeeb391c4885267d9c80ba3a8ea3dfd9e9ea8832.pdf
DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator	https://openreview.net/forum?id=hniLRD_XCA	https://openreview.net/forum?id=hniLRD_XCA	Minghao Han,Jacob Euler-Rolle,Robert K. Katzschmann	ICLR 2022,Poster	The Koopman operator theory linearly describes nonlinear dynamical systems in a high-dimensional functional space and it allows to apply linear control methods to highly nonlinear systems. However, the Koopman operator does not account for any uncertainty in dynamical systems, causing it to perform poorly in real-world...	https://openreview.net/pdf/862602026e43c103de39be4295ff8f7288f3acf2.pdf
Neural Network Approximation based on Hausdorff distance of Tropical Zonotopes	https://openreview.net/forum?id=oiZJwC_fyS	https://openreview.net/forum?id=oiZJwC_fyS	Panagiotis Misiakos,Georgios Smyrnis,George Retsinas,Petros Maragos	ICLR 2022,Poster	In this work we theoretically contribute to neural network approximation by providing a novel tropical geometrical viewpoint to structured neural network compression. In particular, we show that the approximation error between two neural networks with ReLU activations and one hidden layer depends on the Hausdorff dista...	https://openreview.net/pdf/e09efd74b974abec052126ca4cbb787b04fd3265.pdf

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ICLR 2022 International Conference on Learning Representations 2022 Accepted Paper Meta Info Dataset

This dataset is collect from the ICLR 2022 OpenReview website (https://openreview.net/group?id=ICLR.cc/2022/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/iclr2022). For researchers who are interested in doing analysis of ICLR 2022 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 ICLR 2022 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

{
    "title": "Domino: Discovering Systematic Errors with Cross-Modal Embeddings",
    "url": "https://openreview.net/forum?id=FPCMqjI0jXN",
    "detail_url": "https://openreview.net/forum?id=FPCMqjI0jXN",
    "authors": "Sabri Eyuboglu,Maya Varma,Khaled Kamal Saab,Jean-Benoit Delbrouck,Christopher Lee-Messer,Jared Dunnmon,James Zou,Christopher Re",
    "tags": "ICLR 2022,Oral",
    "abstract": "Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address this issue, recent studies have proposed automated slice discovery methods (SDMs), which leverage learned model representations to mine input data for slices on which a model performs poorly. To be useful to a practitioner, these methods must identify slices that are both underperforming and coherent (i.e. united by a human-understandable concept). However, no quantitative evaluation framework currently exists for rigorously assessing SDMs with respect to these criteria. Additionally, prior qualitative evaluations have shown that SDMs often identify slices that are incoherent. In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1,235 slice discovery settings in three input domains (natural images, medical images, and time-series data).\nThen, motivated by the recent development of powerful cross-modal representation learning approaches, we present Domino, an SDM that leverages cross-modal embeddings and a novel error-aware mixture model to discover and describe coherent slices. We find that Domino accurately identifies 36% of the 1,235 slices in our framework -- a 12 percentage point improvement over prior methods. Further, Domino is the first SDM that can provide natural language descriptions of identified slices, correctly generating the exact name of the slice in 35% of settings. ",
    "pdf": "https://openreview.net/pdf/a5ca838a35d810400cfa090453cd85abe02ab6b0.pdf"
}

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ICLR 2022 International Conference on Learning Representations 2022 Accepted Paper Meta Info Dataset

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