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https://proceedings.mlr.press/v202/aamand23a.html | https://proceedings.mlr.press/v202/aamand23a/aamand23a.pdf | https://openreview.net/forum?id=BVomXLJQoH | Data Structures for Density Estimation | https://proceedings.mlr.press/v202/aamand23a.html | Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal | https://proceedings.mlr.press/v202/aamand23a.html | ICML 2023 | We study statistical/computational tradeoffs for the following density estimation problem: given $k$ distributions $v_1, \ldots, v_k$ over a discrete domain of size $n$, and sampling access to a distribution $p$, identify $v_i$ that is "close" to $p$. Our main result is the first data structure that, given a sublinear ... |
https://proceedings.mlr.press/v202/abbas23a.html | https://proceedings.mlr.press/v202/abbas23a/abbas23a.pdf | https://openreview.net/forum?id=IK5SlumdGu | ClusterFuG: Clustering Fully connected Graphs by Multicut | https://proceedings.mlr.press/v202/abbas23a.html | Ahmed Abbas, Paul Swoboda | https://proceedings.mlr.press/v202/abbas23a.html | ICML 2023 | We propose a graph clustering formulation based on multicut (a.k.a. weighted correlation clustering) on the complete graph. Our formulation does not need specification of the graph topology as in the original sparse formulation of multicut, making our approach simpler and potentially better performing. In contrast to u... |
https://proceedings.mlr.press/v202/abbe23a.html | https://proceedings.mlr.press/v202/abbe23a/abbe23a.pdf | https://openreview.net/forum?id=3dqwXb1te4 | Generalization on the Unseen, Logic Reasoning and Degree Curriculum | https://proceedings.mlr.press/v202/abbe23a.html | Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk | https://proceedings.mlr.press/v202/abbe23a.html | ICML 2023 | This paper considers the learning of logical (Boolean) functions with focus on the generalization on the unseen (GOTU) setting, a strong case of out-of-distribution generalization. This is motivated by the fact that the rich combinatorial nature of data in certain reasoning tasks (e.g., arithmetic/logic) makes represen... |
https://proceedings.mlr.press/v202/abedsoltan23a.html | https://proceedings.mlr.press/v202/abedsoltan23a/abedsoltan23a.pdf | https://openreview.net/forum?id=fCyg20LQsL | Toward Large Kernel Models | https://proceedings.mlr.press/v202/abedsoltan23a.html | Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit | https://proceedings.mlr.press/v202/abedsoltan23a.html | ICML 2023 | Recent studies indicate that kernel machines can often perform similarly or better than deep neural networks (DNNs) on small datasets. The interest in kernel machines has been additionally bolstered by the discovery of their equivalence to wide neural networks in certain regimes. However, a key feature of DNNs is their... |
https://proceedings.mlr.press/v202/abels23a.html | https://proceedings.mlr.press/v202/abels23a/abels23a.pdf | https://openreview.net/forum?id=Fd7NCsKLPF | Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making | https://proceedings.mlr.press/v202/abels23a.html | Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowe | https://proceedings.mlr.press/v202/abels23a.html | ICML 2023 | Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases. In this work, we model such changes in depth and breadth of knowledge as a partitioning of the problem space ... |
https://proceedings.mlr.press/v202/acharki23a.html | https://proceedings.mlr.press/v202/acharki23a/acharki23a.pdf | https://openreview.net/forum?id=lJaAPdXgxL | Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects | https://proceedings.mlr.press/v202/acharki23a.html | Naoufal Acharki, Ramiro Lugo, Antoine Bertoncello, Josselin Garnier | https://proceedings.mlr.press/v202/acharki23a.html | ICML 2023 | Conditional Average Treatment Effects (CATE) estimation is one of the main challenges in causal inference with observational data. In addition to Machine Learning based-models, nonparametric estimators called meta-learners have been developed to estimate the CATE with the main advantage of not restraining the estimatio... |
https://proceedings.mlr.press/v202/adams23a.html | https://proceedings.mlr.press/v202/adams23a/adams23a.pdf | https://openreview.net/forum?id=wHPDEyYEps | BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming | https://proceedings.mlr.press/v202/adams23a.html | Steven Adams, Andrea Patane, Morteza Lahijanian, Luca Laurenti | https://proceedings.mlr.press/v202/adams23a.html | ICML 2023 | In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input points $T\subset \mathbb{R}^n$, BNN-DP computes lower and upper bounds on the BNN’s predictions for all the points in $T$. The framework is based... |
https://proceedings.mlr.press/v202/agarwala23a.html | https://proceedings.mlr.press/v202/agarwala23a/agarwala23a.pdf | https://openreview.net/forum?id=5YAP9Ntq3L | SAM operates far from home: eigenvalue regularization as a dynamical phenomenon | https://proceedings.mlr.press/v202/agarwala23a.html | Atish Agarwala, Yann Dauphin | https://proceedings.mlr.press/v202/agarwala23a.html | ICML 2023 | The Sharpness Aware Minimization (SAM) optimization algorithm has been shown to control large eigenvalues of the loss Hessian and provide generalization benefits in a variety of settings. The original motivation for SAM was a modified loss function which penalized sharp minima; subsequent analyses have also focused on ... |
https://proceedings.mlr.press/v202/agarwala23b.html | https://proceedings.mlr.press/v202/agarwala23b/agarwala23b.pdf | https://openreview.net/forum?id=mP79L3pOke | Second-order regression models exhibit progressive sharpening to the edge of stability | https://proceedings.mlr.press/v202/agarwala23b.html | Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington | https://proceedings.mlr.press/v202/agarwala23b.html | ICML 2023 | Recent studies of gradient descent with large step sizes have shown that there is often a regime with an initial increase in the largest eigenvalue of the loss Hessian (progressive sharpening), followed by a stabilization of the eigenvalue near the maximum value which allows convergence (edge of stability). These pheno... |
https://proceedings.mlr.press/v202/agazzi23a.html | https://proceedings.mlr.press/v202/agazzi23a/agazzi23a.pdf | https://openreview.net/forum?id=szQzz2H8er | Global optimality of Elman-type RNNs in the mean-field regime | https://proceedings.mlr.press/v202/agazzi23a.html | Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee | https://proceedings.mlr.press/v202/agazzi23a.html | ICML 2023 | We analyze Elman-type recurrent neural networks (RNNs) and their training in the mean-field regime. Specifically, we show convergence of gradient descent training dynamics of the RNN to the corresponding mean-field formulation in the large width limit. We also show that the fixed points of the limiting infinite-width d... |
https://proceedings.mlr.press/v202/aggarwal23a.html | https://proceedings.mlr.press/v202/aggarwal23a/aggarwal23a.pdf | https://openreview.net/forum?id=kwb6T6LP7f | SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification | https://proceedings.mlr.press/v202/aggarwal23a.html | Pranjal Aggarwal, Ameet Deshpande, Karthik R Narasimhan | https://proceedings.mlr.press/v202/aggarwal23a.html | ICML 2023 | Extreme classification (XC) involves predicting over large numbers of classes (thousands to millions), with real-world applications like news article classification and e-commerce product tagging. The zero-shot version of this task requires generalization to novel classes without additional supervision. In this paper, ... |
https://proceedings.mlr.press/v202/aghabozorgi23a.html | https://proceedings.mlr.press/v202/aghabozorgi23a/aghabozorgi23a.pdf | https://openreview.net/forum?id=CNq0JvrDfw | Adaptive IMLE for Few-shot Pretraining-free Generative Modelling | https://proceedings.mlr.press/v202/aghabozorgi23a.html | Mehran Aghabozorgi, Shichong Peng, Ke Li | https://proceedings.mlr.press/v202/aghabozorgi23a.html | ICML 2023 | Despite their success on large datasets, GANs have been difficult to apply in the few-shot setting, where only a limited number of training examples are provided. Due to mode collapse, GANs tend to ignore some training examples, causing overfitting to a subset of the training dataset, which is small in the first place.... |
https://proceedings.mlr.press/v202/aghajanyan23a.html | https://proceedings.mlr.press/v202/aghajanyan23a/aghajanyan23a.pdf | https://openreview.net/forum?id=2n7dHVhwJf | Scaling Laws for Generative Mixed-Modal Language Models | https://proceedings.mlr.press/v202/aghajanyan23a.html | Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, Luke Zettlemoyer | https://proceedings.mlr.press/v202/aghajanyan23a.html | ICML 2023 | Generative language models define distributions over sequences of tokens that can represent essentially any combination of data modalities (e.g., any permutation of image tokens from VQ-VAEs, speech tokens from HuBERT, BPE tokens for language or code, and so on). To better understand the scaling properties of such mixe... |
https://proceedings.mlr.press/v202/aghbalou23a.html | https://proceedings.mlr.press/v202/aghbalou23a/aghbalou23a.pdf | https://openreview.net/forum?id=Dg5H4Qd0dZ | Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability | https://proceedings.mlr.press/v202/aghbalou23a.html | Anass Aghbalou, Guillaume Staerman | https://proceedings.mlr.press/v202/aghbalou23a.html | ICML 2023 | Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing for a previous task leverage, named the source, into a new one, the target, without requiring access to the source data. Indeed, HTL relies only on a hypothesis learnt from such source data, relieving the hurdle of expansive data storage and pro... |
https://proceedings.mlr.press/v202/aglietti23a.html | https://proceedings.mlr.press/v202/aglietti23a/aglietti23a.pdf | https://openreview.net/forum?id=60bhXDeTos | Constrained Causal Bayesian Optimization | https://proceedings.mlr.press/v202/aglietti23a.html | Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa | https://proceedings.mlr.press/v202/aglietti23a.html | ICML 2023 | We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cCBO first reduces the search space by exploiting the graph structure and, if available, an observational dataset; and then solves the restric... |
https://proceedings.mlr.press/v202/agoritsas23a.html | https://proceedings.mlr.press/v202/agoritsas23a/agoritsas23a.pdf | https://openreview.net/forum?id=DF9aUqGzsV | Explaining the effects of non-convergent MCMC in the training of Energy-Based Models | https://proceedings.mlr.press/v202/agoritsas23a.html | Elisabeth Agoritsas, Giovanni Catania, Aurélien Decelle, Beatriz Seoane | https://proceedings.mlr.press/v202/agoritsas23a.html | ICML 2023 | In this paper, we quantify the impact of using non-convergent Markov chains to train Energy-Based models (EBMs). In particular, we show analytically that EBMs trained with non-persistent short runs to estimate the gradient can perfectly reproduce a set of empirical statistics of the data, not at the level of the equili... |
https://proceedings.mlr.press/v202/aher23a.html | https://proceedings.mlr.press/v202/aher23a/aher23a.pdf | https://openreview.net/forum?id=eYlLlvzngu | Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies | https://proceedings.mlr.press/v202/aher23a.html | Gati V Aher, Rosa I. Arriaga, Adam Tauman Kalai | https://proceedings.mlr.press/v202/aher23a.html | ICML 2023 | We introduce a new type of test, called a Turing Experiment (TE), for evaluating to what extent a given language model, such as GPT models, can simulate different aspects of human behavior. A TE can also reveal consistent distortions in a language model’s simulation of a specific human behavior. Unlike the Turing Test,... |
https://proceedings.mlr.press/v202/ahuja23a.html | https://proceedings.mlr.press/v202/ahuja23a/ahuja23a.pdf | https://openreview.net/forum?id=YiWzhu9pl6 | Interventional Causal Representation Learning | https://proceedings.mlr.press/v202/ahuja23a.html | Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio | https://proceedings.mlr.press/v202/ahuja23a.html | ICML 2023 | Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observational data and structural assumptions (e.g., conditional independence) to identify the latent factors. However, interventional data is prevalent across applications. Can intervent... |
https://proceedings.mlr.press/v202/ailer23a.html | https://proceedings.mlr.press/v202/ailer23a/ailer23a.pdf | https://openreview.net/forum?id=dT7uMuZJjf | Sequential Underspecified Instrument Selection for Cause-Effect Estimation | https://proceedings.mlr.press/v202/ailer23a.html | Elisabeth Ailer, Jason Hartford, Niki Kilbertus | https://proceedings.mlr.press/v202/ailer23a.html | ICML 2023 | Instrumental variable (IV) methods are used to estimate causal effects in settings with unobserved confounding, where we cannot directly experiment on the treatment variable. Instruments are variables which only affect the outcome indirectly via the treatment variable(s). Most IV applications focus on low-dimensional t... |
https://proceedings.mlr.press/v202/aitchison23a.html | https://proceedings.mlr.press/v202/aitchison23a/aitchison23a.pdf | https://openreview.net/forum?id=xRDHjO0YBo | Atari-5: Distilling the Arcade Learning Environment down to Five Games | https://proceedings.mlr.press/v202/aitchison23a.html | Matthew Aitchison, Penny Sweetser, Marcus Hutter | https://proceedings.mlr.press/v202/aitchison23a.html | ICML 2023 | The Arcade Learning Environment (ALE) has become an essential benchmark for assessing the performance of reinforcement learning algorithms. However, the computational cost of generating results on the entire 57-game dataset limits ALE’s use and makes the reproducibility of many results infeasible. We propose a novel so... |
https://proceedings.mlr.press/v202/akhtar23a.html | https://proceedings.mlr.press/v202/akhtar23a/akhtar23a.pdf | https://openreview.net/forum?id=cHZBCZmfSo | Towards credible visual model interpretation with path attribution | https://proceedings.mlr.press/v202/akhtar23a.html | Naveed Akhtar, Mohammad A. A. K. Jalwana | https://proceedings.mlr.press/v202/akhtar23a.html | ICML 2023 | With its inspirational roots in game-theory, path attribution framework stands out among the post-hoc model interpretation techniques due to its axiomatic nature. However, recent developments show that despite being axiomatic, path attribution methods can compute counter-intuitive feature attributions. Not only that, f... |
https://proceedings.mlr.press/v202/alacaoglu23a.html | https://proceedings.mlr.press/v202/alacaoglu23a/alacaoglu23a.pdf | https://openreview.net/forum?id=UZmfIzyTvW | Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data | https://proceedings.mlr.press/v202/alacaoglu23a.html | Ahmet Alacaoglu, Hanbaek Lyu | https://proceedings.mlr.press/v202/alacaoglu23a.html | ICML 2023 | We focus on analyzing the classical stochastic projected gradient methods under a general dependent data sampling scheme for constrained smooth nonconvex optimization. We show the worst-case rate of convergence $\tilde{O}(t^{-1/4})$ and complexity $\tilde{O}(\varepsilon^{-4})$ for achieving an $\varepsilon$-near statio... |
https://proceedings.mlr.press/v202/alam23a.html | https://proceedings.mlr.press/v202/alam23a/alam23a.pdf | https://openreview.net/forum?id=CTZHb6PrHF | Recasting Self-Attention with Holographic Reduced Representations | https://proceedings.mlr.press/v202/alam23a.html | Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt | https://proceedings.mlr.press/v202/alam23a.html | ICML 2023 | In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the $\mathcal{O}(T^2)$ memory and $\mathcal{O}(T^2 H)$ compute costs can make using transformers infeasible. Motivated by problems in malware detection, whe... |
https://proceedings.mlr.press/v202/alghamdi23a.html | https://proceedings.mlr.press/v202/alghamdi23a/alghamdi23a.pdf | https://openreview.net/forum?id=IK7UWsjhUp | The Saddle-Point Method in Differential Privacy | https://proceedings.mlr.press/v202/alghamdi23a.html | Wael Alghamdi, Juan Felipe Gomez, Shahab Asoodeh, Flavio Calmon, Oliver Kosut, Lalitha Sankar | https://proceedings.mlr.press/v202/alghamdi23a.html | ICML 2023 | We characterize the differential privacy guarantees of privacy mechanisms in the large-composition regime, i.e., when a privacy mechanism is sequentially applied a large number of times to sensitive data. Via exponentially tilting the privacy loss random variable, we derive a new formula for the privacy curve expressin... |
https://proceedings.mlr.press/v202/ali-mehmeti-gopel23a.html | https://proceedings.mlr.press/v202/ali-mehmeti-gopel23a/ali-mehmeti-gopel23a.pdf | https://openreview.net/forum?id=tAa6ivLs6D | Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think | https://proceedings.mlr.press/v202/ali-mehmeti-gopel23a.html | Christian H.X. Ali Mehmeti-Göpel, Jan Disselhoff | https://proceedings.mlr.press/v202/ali-mehmeti-gopel23a.html | ICML 2023 | We perform an empirical study of the behaviour of deep networks when fully linearizing some of its feature channels through a sparsity prior on the overall number of nonlinear units in the network. In experiments on image classification and machine translation tasks, we investigate how much we can simplify the network ... |
https://proceedings.mlr.press/v202/allingham23a.html | https://proceedings.mlr.press/v202/allingham23a/allingham23a.pdf | https://openreview.net/forum?id=6MU5xdrO7t | A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models | https://proceedings.mlr.press/v202/allingham23a.html | James Urquhart Allingham, Jie Ren, Michael W Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan | https://proceedings.mlr.press/v202/allingham23a.html | ICML 2023 | Contrastively trained text-image models have the remarkable ability to perform zero-shot classification, that is, classifying previously unseen images into categories that the model has never been explicitly trained to identify. However, these zero-shot classifiers need prompt engineering to achieve high accuracy. Prom... |
https://proceedings.mlr.press/v202/allouah23a.html | https://proceedings.mlr.press/v202/allouah23a/allouah23a.pdf | https://openreview.net/forum?id=5WxdnjlCv7 | On the Privacy-Robustness-Utility Trilemma in Distributed Learning | https://proceedings.mlr.press/v202/allouah23a.html | Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan | https://proceedings.mlr.press/v202/allouah23a.html | ICML 2023 | The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied independently in distributed ML, their synthesis remains poorly ... |
https://proceedings.mlr.press/v202/alparslan23a.html | https://proceedings.mlr.press/v202/alparslan23a/alparslan23a.pdf | https://openreview.net/forum?id=O3adXl7uBw | Differentially Private Distributed Bayesian Linear Regression with MCMC | https://proceedings.mlr.press/v202/alparslan23a.html | Baris Alparslan, Sinan Yıldırım, Ilker Birbil | https://proceedings.mlr.press/v202/alparslan23a.html | ICML 2023 | We propose a novel Bayesian inference framework for distributed differentially private linear regression. We consider a distributed setting where multiple parties hold parts of the data and share certain summary statistics of their portions in privacy-preserving noise. We develop a novel generative statistical model fo... |
https://proceedings.mlr.press/v202/altamirano23a.html | https://proceedings.mlr.press/v202/altamirano23a/altamirano23a.pdf | https://openreview.net/forum?id=jWmHbfKeQF | Robust and Scalable Bayesian Online Changepoint Detection | https://proceedings.mlr.press/v202/altamirano23a.html | Matias Altamirano, Francois-Xavier Briol, Jeremias Knoblauch | https://proceedings.mlr.press/v202/altamirano23a.html | ICML 2023 | This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has key advantages over previous work: it provides provable robustness by leveraging the generalised Bayesian perspective, and also addresses the scalability issues of previous attempts. Spe... |
https://proceedings.mlr.press/v202/altekruger23a.html | https://proceedings.mlr.press/v202/altekruger23a/altekruger23a.pdf | https://openreview.net/forum?id=Ur1Eckuj3V | Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels | https://proceedings.mlr.press/v202/altekruger23a.html | Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl | https://proceedings.mlr.press/v202/altekruger23a.html | ICML 2023 | Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals with non-smooth Riesz kernels show a rich structure as singular measures can become absolutely continuous ones and conversely. In this paper we contribute to the understanding of such flows. We propose to approximate the backward scheme of Jordan,... |
https://proceedings.mlr.press/v202/amani23a.html | https://proceedings.mlr.press/v202/amani23a/amani23a.pdf | https://openreview.net/forum?id=vTSLiw1GfJ | Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost | https://proceedings.mlr.press/v202/amani23a.html | Sanae Amani, Tor Lattimore, András György, Lin Yang | https://proceedings.mlr.press/v202/amani23a.html | ICML 2023 | We study distributed contextual linear bandits with stochastic contexts, where $N$ agents/learners act cooperatively to solve a linear bandit-optimization problem with $d$-dimensional features over the course of $T$ rounds. For this problem, we derive the first ever information-theoretic lower bound $\Omega(dN)$ on the... |
https://proceedings.mlr.press/v202/amin23a.html | https://proceedings.mlr.press/v202/amin23a/amin23a.pdf | https://openreview.net/forum?id=8LdBTjylEw | A Kernelized Stein Discrepancy for Biological Sequences | https://proceedings.mlr.press/v202/amin23a.html | Alan Nawzad Amin, Eli N Weinstein, Debora Susan Marks | https://proceedings.mlr.press/v202/amin23a.html | ICML 2023 | Generative models of biological sequences are a powerful tool for learning from complex sequence data, predicting the effects of mutations, and designing novel biomolecules with desired properties. To evaluate generative models it is important to accurately measure differences between high-dimensional distributions. In... |
https://proceedings.mlr.press/v202/amortila23a.html | https://proceedings.mlr.press/v202/amortila23a/amortila23a.pdf | https://openreview.net/forum?id=OT6gRRMmcE | The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation | https://proceedings.mlr.press/v202/amortila23a.html | Philip Amortila, Nan Jiang, Csaba Szepesvari | https://proceedings.mlr.press/v202/amortila23a.html | ICML 2023 | Theoretical guarantees in reinforcement learning (RL) are known to suffer multiplicative blow-up factors with respect to the misspecification error of function approximation. Yet, the nature of such approximation factors—especially their optimal form in a given learning problem—is poorly understood. In this paper we st... |
https://proceedings.mlr.press/v202/amos23a.html | https://proceedings.mlr.press/v202/amos23a/amos23a.pdf | https://openreview.net/forum?id=vinsvrSJmd | Meta Optimal Transport | https://proceedings.mlr.press/v202/amos23a.html | Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko | https://proceedings.mlr.press/v202/amos23a.html | ICML 2023 | We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT. This helps repeatedly solve similar OT problems between different measures by leveraging the knowledge and information present from past problems to rapidly predict and solve new problems. O... |
https://proceedings.mlr.press/v202/anagnostides23a.html | https://proceedings.mlr.press/v202/anagnostides23a/anagnostides23a.pdf | https://openreview.net/forum?id=FK18BRc1vL | Near-Optimal $Φ$-Regret Learning in Extensive-Form Games | https://proceedings.mlr.press/v202/anagnostides23a.html | Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm | https://proceedings.mlr.press/v202/anagnostides23a.html | ICML 2023 | In this paper, we establish efficient and uncoupled learning dynamics so that, when employed by all players in multiplayer perfect-recall imperfect-information extensive-form games, the trigger regret of each player grows as $O(\log T)$ after $T$ repetitions of play. This improves exponentially over the prior best know... |
https://proceedings.mlr.press/v202/andriushchenko23a.html | https://proceedings.mlr.press/v202/andriushchenko23a/andriushchenko23a.pdf | https://openreview.net/forum?id=VZp9X410D3 | A Modern Look at the Relationship between Sharpness and Generalization | https://proceedings.mlr.press/v202/andriushchenko23a.html | Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion | https://proceedings.mlr.press/v202/andriushchenko23a.html | ICML 2023 | Sharpness of minima is a promising quantity that can correlate with generalization in deep networks and, when optimized during training, can improve generalization. However, standard sharpness is not invariant under reparametrizations of neural networks, and, to fix this, reparametrization-invariant sharpness definitio... |
https://proceedings.mlr.press/v202/andriushchenko23b.html | https://proceedings.mlr.press/v202/andriushchenko23b/andriushchenko23b.pdf | https://openreview.net/forum?id=DnTuz0ziwN | SGD with Large Step Sizes Learns Sparse Features | https://proceedings.mlr.press/v202/andriushchenko23b.html | Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion | https://proceedings.mlr.press/v202/andriushchenko23b.html | ICML 2023 | We showcase important features of the dynamics of the Stochastic Gradient Descent (SGD) in the training of neural networks. We present empirical observations that commonly used large step sizes (i) may lead the iterates to jump from one side of a valley to the other causing loss stabilization, and (ii) this stabilizati... |
https://proceedings.mlr.press/v202/ansari23a.html | https://proceedings.mlr.press/v202/ansari23a/ansari23a.pdf | https://openreview.net/forum?id=GTos8jbYUa | Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series | https://proceedings.mlr.press/v202/ansari23a.html | Abdul Fatir Ansari, Alvin Heng, Andre Lim, Harold Soh | https://proceedings.mlr.press/v202/ansari23a.html | ICML 2023 | Learning accurate predictive models of real-world dynamic phenomena (e.g., climate, biological) remains a challenging task. One key issue is that the data generated by both natural and artificial processes often comprise time series that are irregularly sampled and/or contain missing observations. In this work, we prop... |
https://proceedings.mlr.press/v202/antoniadis23a.html | https://proceedings.mlr.press/v202/antoniadis23a/antoniadis23a.pdf | https://openreview.net/forum?id=NG8f2j1EKb | Paging with Succinct Predictions | https://proceedings.mlr.press/v202/antoniadis23a.html | Antonios Antoniadis, Joan Boyar, Marek Elias, Lene Monrad Favrholdt, Ruben Hoeksma, Kim S. Larsen, Adam Polak, Bertrand Simon | https://proceedings.mlr.press/v202/antoniadis23a.html | ICML 2023 | Paging is a prototypical problem in the area of online algorithms. It has also played a central role in the development of learning-augmented algorithms. Previous work on learning-augmented paging has investigated predictions on (i) when the current page will be requested again (reoccurrence predictions), (ii) the curr... |
https://proceedings.mlr.press/v202/antoniadis23b.html | https://proceedings.mlr.press/v202/antoniadis23b/antoniadis23b.pdf | https://openreview.net/forum?id=HqQIt6mt5B | Mixing Predictions for Online Metric Algorithms | https://proceedings.mlr.press/v202/antoniadis23b.html | Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon | https://proceedings.mlr.press/v202/antoniadis23b.html | ICML 2023 | A major technique in learning-augmented online algorithms is combining multiple algorithms or predictors. Since the performance of each predictor may vary over time, it is desirable to use not the single best predictor as a benchmark, but rather a dynamic combination which follows different predictors at different time... |
https://proceedings.mlr.press/v202/aouali23a.html | https://proceedings.mlr.press/v202/aouali23a/aouali23a.pdf | https://openreview.net/forum?id=LJ9iKElXpl | Exponential Smoothing for Off-Policy Learning | https://proceedings.mlr.press/v202/aouali23a.html | Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba | https://proceedings.mlr.press/v202/aouali23a.html | ICML 2023 | Off-policy learning (OPL) aims at finding improved policies from logged bandit data, often by minimizing the inverse propensity scoring (IPS) estimator of the risk. In this work, we investigate a smooth regularization for IPS, for which we derive a two-sided PAC-Bayes generalization bound. The bound is tractable, scala... |
https://proceedings.mlr.press/v202/arbas23a.html | https://proceedings.mlr.press/v202/arbas23a/arbas23a.pdf | https://openreview.net/forum?id=b6Hxt4Jw10 | Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models | https://proceedings.mlr.press/v202/arbas23a.html | Jamil Arbas, Hassan Ashtiani, Christopher Liaw | https://proceedings.mlr.press/v202/arbas23a.html | ICML 2023 | We study the problem of privately estimating the parameters of $d$-dimensional Gaussian Mixture Models (GMMs) with $k$ components. For this, we develop a technique to reduce the problem to its non-private counterpart. This allows us to privatize existing non-private algorithms in a blackbox manner, while incurring only... |
https://proceedings.mlr.press/v202/arisaka23a.html | https://proceedings.mlr.press/v202/arisaka23a/arisaka23a.pdf | https://openreview.net/forum?id=2MbU8qSWL1 | Principled Acceleration of Iterative Numerical Methods Using Machine Learning | https://proceedings.mlr.press/v202/arisaka23a.html | Sohei Arisaka, Qianxiao Li | https://proceedings.mlr.press/v202/arisaka23a.html | ICML 2023 | Iterative methods are ubiquitous in large-scale scientific computing applications, and a number of approaches based on meta-learning have been recently proposed to accelerate them. However, a systematic study of these approaches and how they differ from meta-learning is lacking. In this paper, we propose a framework to... |
https://proceedings.mlr.press/v202/arora23a.html | https://proceedings.mlr.press/v202/arora23a/arora23a.pdf | https://openreview.net/forum?id=kOUBFwYd2D | Faster Rates of Convergence to Stationary Points in Differentially Private Optimization | https://proceedings.mlr.press/v202/arora23a.html | Raman Arora, Raef Bassily, Tomás González, Cristóbal A Guzmán, Michael Menart, Enayat Ullah | https://proceedings.mlr.press/v202/arora23a.html | ICML 2023 | We study the problem of approximating stationary points of Lipschitz and smooth functions under $(\varepsilon,\delta)$-differential privacy (DP) in both the finite-sum and stochastic settings. A point $\widehat{w}$ is called an $\alpha$-stationary point of a function $F:\mathbb{R}^d\rightarrow\mathbb{R}$ if $\|\nabla F... |
https://proceedings.mlr.press/v202/asadi23a.html | https://proceedings.mlr.press/v202/asadi23a/asadi23a.pdf | https://openreview.net/forum?id=ywwdhhqNj7 | Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning | https://proceedings.mlr.press/v202/asadi23a.html | Nader Asadi, Mohammadreza Davari, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky | https://proceedings.mlr.press/v202/asadi23a.html | ICML 2023 | In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent best-performing methods utilize various forms of prior task data, e.g. a replay buffer, to tackle the catastrophic forgetting problem. Having access to previous task data can be r... |
https://proceedings.mlr.press/v202/asi23a.html | https://proceedings.mlr.press/v202/asi23a/asi23a.pdf | https://openreview.net/forum?id=SjwWVAyYKh | Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime | https://proceedings.mlr.press/v202/asi23a.html | Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar | https://proceedings.mlr.press/v202/asi23a.html | ICML 2023 | We consider online learning problems in the realizable setting, where there is a zero-loss solution, and propose new Differentially Private (DP) algorithms that obtain near-optimal regret bounds. For the problem of online prediction from experts, we design new algorithms that obtain near-optimal regret $O \big( \vareps... |
https://proceedings.mlr.press/v202/asi23b.html | https://proceedings.mlr.press/v202/asi23b/asi23b.pdf | https://openreview.net/forum?id=9viDfxnY3q | From Robustness to Privacy and Back | https://proceedings.mlr.press/v202/asi23b.html | Hilal Asi, Jonathan Ullman, Lydia Zakynthinou | https://proceedings.mlr.press/v202/asi23b.html | ICML 2023 | We study the relationship between two desiderata of algorithms in statistical inference and machine learning—differential privacy and robustness to adversarial data corruptions. Their conceptual similarity was first observed by Dwork and Lei (STOC 2009), who observed that private algorithms satisfy robustness, and gave... |
https://proceedings.mlr.press/v202/attia23a.html | https://proceedings.mlr.press/v202/attia23a/attia23a.pdf | https://openreview.net/forum?id=X7jMTrwuCz | SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance | https://proceedings.mlr.press/v202/attia23a.html | Amit Attia, Tomer Koren | https://proceedings.mlr.press/v202/attia23a.html | ICML 2023 | We study Stochastic Gradient Descent with AdaGrad stepsizes: a popular adaptive (self-tuning) method for first-order stochastic optimization. Despite being well studied, existing analyses of this method suffer from various shortcomings: they either assume some knowledge of the problem parameters, impose strong global L... |
https://proceedings.mlr.press/v202/attias23a.html | https://proceedings.mlr.press/v202/attias23a/attias23a.pdf | https://openreview.net/forum?id=fcDq3BIbe9 | Adversarially Robust PAC Learnability of Real-Valued Functions | https://proceedings.mlr.press/v202/attias23a.html | Idan Attias, Steve Hanneke | https://proceedings.mlr.press/v202/attias23a.html | ICML 2023 | We study robustness to test-time adversarial attacks in the regression setting with $\ell_p$ losses and arbitrary perturbation sets. We address the question of which function classes are PAC learnable in this setting. We show that classes of finite fat-shattering dimension are learnable in both the realizable and agnos... |
https://proceedings.mlr.press/v202/atzeni23a.html | https://proceedings.mlr.press/v202/atzeni23a/atzeni23a.pdf | https://openreview.net/forum?id=tE3BMOyUl5 | Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning | https://proceedings.mlr.press/v202/atzeni23a.html | Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas | https://proceedings.mlr.press/v202/atzeni23a.html | ICML 2023 | The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most recent language-complete instantiation (LARC) has been postulated as an important step towards general AI. Yet, even state-of-the-art machine learning models struggle to achieve meaningful performance on these problems, falling behind non-learning ... |
https://proceedings.mlr.press/v202/atzmon23a.html | https://proceedings.mlr.press/v202/atzmon23a/atzmon23a.pdf | https://openreview.net/forum?id=BJc95DyFNG | Learning to Initiate and Reason in Event-Driven Cascading Processes | https://proceedings.mlr.press/v202/atzmon23a.html | Yuval Atzmon, Eli Meirom, Shie Mannor, Gal Chechik | https://proceedings.mlr.press/v202/atzmon23a.html | ICML 2023 | Training agents to control a dynamic environment is a fundamental task in AI. In many environments, the dynamics can be summarized by a small set of events that capture the semantic behavior of the system. Typically, these events form chains or cascades. We often wish to change the system behavior using a single interv... |
https://proceedings.mlr.press/v202/aubert23a.html | https://proceedings.mlr.press/v202/aubert23a/aubert23a.pdf | https://openreview.net/forum?id=YvrxWGWg9E | On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm | https://proceedings.mlr.press/v202/aubert23a.html | Julien Aubert, Luc Lehéricy, Patricia Reynaud-Bouret | https://proceedings.mlr.press/v202/aubert23a.html | ICML 2023 | When fitting the learning data of an individual to algorithm-like learning models, the observations are so dependent and non-stationary that one may wonder what the classical Maximum Likelihood Estimator (MLE) could do, even if it is the usual tool applied to experimental cognition. Our objective in this work is to sho... |
https://proceedings.mlr.press/v202/avdeyev23a.html | https://proceedings.mlr.press/v202/avdeyev23a/avdeyev23a.pdf | https://openreview.net/forum?id=O3jUIakvK7 | Dirichlet Diffusion Score Model for Biological Sequence Generation | https://proceedings.mlr.press/v202/avdeyev23a.html | Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, Jian Zhou | https://proceedings.mlr.press/v202/avdeyev23a.html | ICML 2023 | Designing biological sequences is an important challenge that requires satisfying complex constraints and thus is a natural problem to address with deep generative modeling. Diffusion generative models have achieved considerable success in many applications. Score-based generative stochastic differential equations (SDE... |
https://proceedings.mlr.press/v202/axiotis23a.html | https://proceedings.mlr.press/v202/axiotis23a/axiotis23a.pdf | https://openreview.net/forum?id=a4bMHPm0Ji | Gradient Descent Converges Linearly for Logistic Regression on Separable Data | https://proceedings.mlr.press/v202/axiotis23a.html | Kyriakos Axiotis, Maxim Sviridenko | https://proceedings.mlr.press/v202/axiotis23a.html | ICML 2023 | We show that running gradient descent with variable learning rate guarantees loss $f(x) ≤ 1.1 \cdot f(x^*)+\epsilon$ for the logistic regression objective, where the error $\epsilon$ decays exponentially with the number of iterations and polynomially with the magnitude of the entries of an arbitrary fixed solution $x$.... |
https://proceedings.mlr.press/v202/ayme23a.html | https://proceedings.mlr.press/v202/ayme23a/ayme23a.pdf | https://openreview.net/forum?id=gfSLvfVf0w | Naive imputation implicitly regularizes high-dimensional linear models | https://proceedings.mlr.press/v202/ayme23a.html | Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet | https://proceedings.mlr.press/v202/ayme23a.html | ICML 2023 | Two different approaches exist to handle missing values for prediction: either imputation, prior to fitting any predictive algorithms, or dedicated methods able to natively incorporate missing values. While imputation is widely (and easily) use, it is unfortunately biased when low-capacity predictors (such as linear mo... |
https://proceedings.mlr.press/v202/azabou23a.html | https://proceedings.mlr.press/v202/azabou23a/azabou23a.pdf | https://openreview.net/forum?id=lXczFIwQkv | Half-Hop: A graph upsampling approach for slowing down message passing | https://proceedings.mlr.press/v202/azabou23a.html | Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Veličković, Eva L Dyer | https://proceedings.mlr.press/v202/azabou23a.html | ICML 2023 | Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different classes. In this work, we introduce a simple yet general framework for improving learning in message... |
https://proceedings.mlr.press/v202/azad23a.html | https://proceedings.mlr.press/v202/azad23a/azad23a.pdf | https://openreview.net/forum?id=wagsJnR5GO | CLUTR: Curriculum Learning via Unsupervised Task Representation Learning | https://proceedings.mlr.press/v202/azad23a.html | Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust, Pieter Abbeel, Ion Stoica | https://proceedings.mlr.press/v202/azad23a.html | ICML 2023 | Reinforcement Learning (RL) algorithms are often known for sample inefficiency and difficult generalization. Recently, Unsupervised Environment Design (UED) emerged as a new paradigm for zero-shot generalization by simultaneously learning a task distribution and agent policies on the generated tasks. This is a non-stat... |
https://proceedings.mlr.press/v202/baek23a.html | https://proceedings.mlr.press/v202/baek23a/baek23a.pdf | https://openreview.net/forum?id=GXHL8ZS1GX | Personalized Subgraph Federated Learning | https://proceedings.mlr.press/v202/baek23a.html | Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, Sung Ju Hwang | https://proceedings.mlr.press/v202/baek23a.html | ICML 2023 | Subgraphs of a larger global graph may be distributed across multiple devices, and only locally accessible due to privacy restrictions, although there may be links between subgraphs. Recently proposed subgraph Federated Learning (FL) methods deal with those missing links across local subgraphs while distributively trai... |
https://proceedings.mlr.press/v202/baevski23a.html | https://proceedings.mlr.press/v202/baevski23a/baevski23a.pdf | https://openreview.net/forum?id=Jc5QwxfyyQ | Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language | https://proceedings.mlr.press/v202/baevski23a.html | Alexei Baevski, Arun Babu, Wei-Ning Hsu, Michael Auli | https://proceedings.mlr.press/v202/baevski23a.html | ICML 2023 | Current self-supervised learning algorithms are often modality-specific and require large amounts of computational resources. To address these issues, we increase the training efficiency of data2vec, a learning objective that generalizes across several modalities. We do not encode masked tokens, use a fast convolutiona... |
https://proceedings.mlr.press/v202/baey23a.html | https://proceedings.mlr.press/v202/baey23a/baey23a.pdf | https://openreview.net/forum?id=ikbUw7okHD | Efficient preconditioned stochastic gradient descent for estimation in latent variable models | https://proceedings.mlr.press/v202/baey23a.html | Charlotte Baey, Maud Delattre, Estelle Kuhn, Jean-Benoist Leger, Sarah Lemler | https://proceedings.mlr.press/v202/baey23a.html | ICML 2023 | Latent variable models are powerful tools for modeling complex phenomena involving in particular partially observed data, unobserved variables or underlying complex unknown structures. Inference is often difficult due to the latent structure of the model. To deal with parameter estimation in the presence of latent vari... |
https://proceedings.mlr.press/v202/bai23a.html | https://proceedings.mlr.press/v202/bai23a/bai23a.pdf | https://openreview.net/forum?id=3FydczZwkJ | Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection | https://proceedings.mlr.press/v202/bai23a.html | Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D Nowak, Yixuan Li | https://proceedings.mlr.press/v202/bai23a.html | ICML 2023 | Modern machine learning models deployed in the wild can encounter both covariate and semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and OOD detection respectively. While both problems have received significant research attention lately, they have been pursued independently. Thi... |
https://proceedings.mlr.press/v202/bai23b.html | https://proceedings.mlr.press/v202/bai23b/bai23b.pdf | https://openreview.net/forum?id=KTJ6E8t9Cy | Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization | https://proceedings.mlr.press/v202/bai23b.html | Yushi Bai, Xin Lv, Juanzi Li, Lei Hou | https://proceedings.mlr.press/v202/bai23b.html | ICML 2023 | Answering complex logical queries on incomplete knowledge graphs is a challenging task, and has been widely studied. Embedding-based methods require training on complex queries and may not generalize well to out-of-distribution query structures. Recent work frames this task as an end-to-end optimization problem, and it... |
https://proceedings.mlr.press/v202/bai23c.html | https://proceedings.mlr.press/v202/bai23c/bai23c.pdf | https://openreview.net/forum?id=ftLm9QAqwc | Linear optimal partial transport embedding | https://proceedings.mlr.press/v202/bai23c.html | Yikun Bai, Ivan Vladimir Medri, Rocio Diaz Martin, Rana Shahroz, Soheil Kolouri | https://proceedings.mlr.press/v202/bai23c.html | ICML 2023 | Optimal transport (OT) has gained popularity due to its various applications in fields such as machine learning, statistics, and signal processing. However, the balanced mass requirement limits its performance in practical problems. To address these limitations, variants of the OT problem, including unbalanced OT, Opti... |
https://proceedings.mlr.press/v202/baker23a.html | https://proceedings.mlr.press/v202/baker23a/baker23a.pdf | https://openreview.net/forum?id=Q8k4WzGgnK | Implicit Graph Neural Networks: A Monotone Operator Viewpoint | https://proceedings.mlr.press/v202/baker23a.html | Justin Baker, Qingsong Wang, Cory D Hauck, Bao Wang | https://proceedings.mlr.press/v202/baker23a.html | ICML 2023 | Implicit graph neural networks (IGNNs) – that solve a fixed-point equilibrium equation using Picard iteration for representation learning – have shown remarkable performance in learning long-range dependencies (LRD) in the underlying graphs. However, IGNNs suffer from several issues, including 1) their expressivity is ... |
https://proceedings.mlr.press/v202/bakshi23a.html | https://proceedings.mlr.press/v202/bakshi23a/bakshi23a.pdf | https://openreview.net/forum?id=lxRIOSlTbb | Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems | https://proceedings.mlr.press/v202/bakshi23a.html | Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau | https://proceedings.mlr.press/v202/bakshi23a.html | ICML 2023 | Recently Chen and Poor initiated the study of learning mixtures of linear dynamical systems. While linear dynamical systems already have wide-ranging applications in modeling time-series data, using mixture models can lead to a better fit or even a richer understanding of underlying subpopulations represented in the da... |
https://proceedings.mlr.press/v202/balabanov23a.html | https://proceedings.mlr.press/v202/balabanov23a/balabanov23a.pdf | https://openreview.net/forum?id=EMN99LtfYA | Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures | https://proceedings.mlr.press/v202/balabanov23a.html | Oleg Balabanov, Matthias Beaupère, Laura Grigori, Victor Lederer | https://proceedings.mlr.press/v202/balabanov23a.html | ICML 2023 | This article introduces a novel structured random matrix composed blockwise from subsampled randomized Hadamard transforms (SRHTs). The block SRHT is expected to outperform well-known dimension reduction maps, including SRHT and Gaussian matrices on distributed architectures. We prove that a block SRHT with enough rows... |
https://proceedings.mlr.press/v202/ball23a.html | https://proceedings.mlr.press/v202/ball23a/ball23a.pdf | https://openreview.net/forum?id=h11j9w1ucU | Efficient Online Reinforcement Learning with Offline Data | https://proceedings.mlr.press/v202/ball23a.html | Philip J. Ball, Laura Smith, Ilya Kostrikov, Sergey Levine | https://proceedings.mlr.press/v202/ball23a.html | ICML 2023 | Sample efficiency and exploration remain major challenges in online reinforcement learning (RL). A powerful approach that can be applied to address these issues is the inclusion of offline data, such as prior trajectories from a human expert or a sub-optimal exploration policy. Previous methods have relied on extensive... |
https://proceedings.mlr.press/v202/ballu23a.html | https://proceedings.mlr.press/v202/ballu23a/ballu23a.pdf | https://openreview.net/forum?id=ImQC3p9wlm | Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes | https://proceedings.mlr.press/v202/ballu23a.html | Marin Ballu, Quentin Berthet | https://proceedings.mlr.press/v202/ballu23a.html | ICML 2023 | Optimal transport is an important tool in machine learning, allowing to capture geometric properties of the data through a linear program on transport polytopes. We present a single-loop optimization algorithm for minimizing general convex objectives on these domains, utilizing the principles of Sinkhorn matrix scaling... |
https://proceedings.mlr.press/v202/balogh23a.html | https://proceedings.mlr.press/v202/balogh23a/balogh23a.pdf | https://openreview.net/forum?id=sFqfXphJh5 | On the Functional Similarity of Robust and Non-Robust Neural Representations | https://proceedings.mlr.press/v202/balogh23a.html | András Balogh, Márk Jelasity | https://proceedings.mlr.press/v202/balogh23a.html | ICML 2023 | Model stitching—where the internal representations of two neural networks are aligned linearly—helped demonstrate that the representations of different neural networks for the same task are surprisingly similar in a functional sense. At the same time, the representations of adversarially robust networks are considered ... |
https://proceedings.mlr.press/v202/balseiro23a.html | https://proceedings.mlr.press/v202/balseiro23a/balseiro23a.pdf | https://openreview.net/forum?id=5h42xM0pwn | Robust Budget Pacing with a Single Sample | https://proceedings.mlr.press/v202/balseiro23a.html | Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang | https://proceedings.mlr.press/v202/balseiro23a.html | ICML 2023 | Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inherent non-stationarity in an advertiser’s value and also competing advertisers’ values over time, a commonly used approach is to learn a target expenditure plan that specifies ... |
https://proceedings.mlr.press/v202/banihashem23a.html | https://proceedings.mlr.press/v202/banihashem23a/banihashem23a.pdf | https://openreview.net/forum?id=2hF9MnBfUk | Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time | https://proceedings.mlr.press/v202/banihashem23a.html | Kiarash Banihashem, Leyla Biabani, Samira Goudarzi, Mohammadtaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh | https://proceedings.mlr.press/v202/banihashem23a.html | ICML 2023 | Maximizing a monotone submodular function under cardinality constraint $k$ is a core problem in machine learning and database with many basic applications, including video and data summarization, recommendation systems, feature extraction, exemplar clustering, and coverage problems. We study this classic problem in the... |
https://proceedings.mlr.press/v202/bao23a.html | https://proceedings.mlr.press/v202/bao23a/bao23a.pdf | https://openreview.net/forum?id=Urp3atR1Z3 | One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale | https://proceedings.mlr.press/v202/bao23a.html | Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu | https://proceedings.mlr.press/v202/bao23a.html | ICML 2023 | This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is – learning diffusion models for marginal, conditional, and joint distributions can be unified as predicting the noise in the perturbed data, where the pe... |
https://proceedings.mlr.press/v202/bao23b.html | https://proceedings.mlr.press/v202/bao23b/bao23b.pdf | https://openreview.net/forum?id=rnNBSMOWvA | Optimizing the Collaboration Structure in Cross-Silo Federated Learning | https://proceedings.mlr.press/v202/bao23b.html | Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He | https://proceedings.mlr.press/v202/bao23b.html | ICML 2023 | In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized. Through utilizing more training data, FL suffers from the potential negative transfer problem: the global FL model may even perform worse than the models trained with local data onl... |
https://proceedings.mlr.press/v202/bar-tal23a.html | https://proceedings.mlr.press/v202/bar-tal23a/bar-tal23a.pdf | https://openreview.net/forum?id=D4ajVWmgLB | MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation | https://proceedings.mlr.press/v202/bar-tal23a.html | Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel | https://proceedings.mlr.press/v202/bar-tal23a.html | ICML 2023 | Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-... |
https://proceedings.mlr.press/v202/barakat23a.html | https://proceedings.mlr.press/v202/barakat23a/barakat23a.pdf | https://openreview.net/forum?id=ZnHXYHx70x | Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space | https://proceedings.mlr.press/v202/barakat23a.html | Anas Barakat, Ilyas Fatkhullin, Niao He | https://proceedings.mlr.press/v202/barakat23a.html | ICML 2023 | We consider the reinforcement learning (RL) problem with general utilities which consists in maximizing a function of the state-action occupancy measure. Beyond the standard cumulative reward RL setting, this problem includes as particular cases constrained RL, pure exploration and learning from demonstrations among ot... |
https://proceedings.mlr.press/v202/barbiero23a.html | https://proceedings.mlr.press/v202/barbiero23a/barbiero23a.pdf | https://openreview.net/forum?id=KbvON8xOCJ | Interpretable Neural-Symbolic Concept Reasoning | https://proceedings.mlr.press/v202/barbiero23a.html | Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frederic Precioso, Mateja Jamnik, Giuseppe Marra | https://proceedings.mlr.press/v202/barbiero23a.html | ICML 2023 | Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust. Concept-based models aim to address this issue by learning tasks based on a set of human-understandable concepts. However, state-of-the-art concept-based models rely on high-dimensional concept embe... |
https://proceedings.mlr.press/v202/bartan23a.html | https://proceedings.mlr.press/v202/bartan23a/bartan23a.pdf | https://openreview.net/forum?id=GN9bGEWvkx | Moccasin: Efficient Tensor Rematerialization for Neural Networks | https://proceedings.mlr.press/v202/bartan23a.html | Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina | https://proceedings.mlr.press/v202/bartan23a.html | ICML 2023 | The deployment and training of neural networks on edge computing devices pose many challenges. The low memory nature of edge devices is often one of the biggest limiting factors encountered in the deployment of large neural network models. Tensor rematerialization or recompute is a way to address high memory requiremen... |
https://proceedings.mlr.press/v202/bassily23a.html | https://proceedings.mlr.press/v202/bassily23a/bassily23a.pdf | https://openreview.net/forum?id=4UStsbnfVT | User-level Private Stochastic Convex Optimization with Optimal Rates | https://proceedings.mlr.press/v202/bassily23a.html | Raef Bassily, Ziteng Sun | https://proceedings.mlr.press/v202/bassily23a.html | ICML 2023 | We study the problem of differentially private (DP) stochastic convex optimization (SCO) under the notion of user-level differential privacy. In this problem, there are $n$ users, each contributing $m>1$ samples to the input dataset of the private SCO algorithm, and the notion of indistinguishability embedded in DP is ... |
https://proceedings.mlr.press/v202/basu23a.html | https://proceedings.mlr.press/v202/basu23a/basu23a.pdf | https://openreview.net/forum?id=0bR5JuxaoN | A Statistical Perspective on Retrieval-Based Models | https://proceedings.mlr.press/v202/basu23a.html | Soumya Basu, Ankit Singh Rawat, Manzil Zaheer | https://proceedings.mlr.press/v202/basu23a.html | ICML 2023 | Many modern high-performing machine learning models increasingly rely on scaling up models, e.g., transformer networks. Simultaneously, a parallel line of work aims to improve the model performance by augmenting an input instance with other (labeled) instances during inference. Examples of such augmentations include ta... |
https://proceedings.mlr.press/v202/bauer23a.html | https://proceedings.mlr.press/v202/bauer23a/bauer23a.pdf | https://openreview.net/forum?id=thUjOwfzzv | Human-Timescale Adaptation in an Open-Ended Task Space | https://proceedings.mlr.press/v202/bauer23a.html | Jakob Bauer, Kate Baumli, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez-Nieves, Nemanja R... | https://proceedings.mlr.press/v202/bauer23a.html | ICML 2023 | Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL). In this work, we demonstrate that training an RL agent at scale leads to a general in-context learning algorithm th... |
https://proceedings.mlr.press/v202/baum23a.html | https://proceedings.mlr.press/v202/baum23a/baum23a.pdf | https://openreview.net/forum?id=XxMRhjbDGq | A Kernel Stein Test of Goodness of Fit for Sequential Models | https://proceedings.mlr.press/v202/baum23a.html | Jerome Baum, Heishiro Kanagawa, Arthur Gretton | https://proceedings.mlr.press/v202/baum23a.html | ICML 2023 | We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length sequences. The proposed measure is an instance of the kernel Stein discrepancy (KSD), which has been used to construct goodness-of-fit tests fo... |
https://proceedings.mlr.press/v202/bechavod23a.html | https://proceedings.mlr.press/v202/bechavod23a/bechavod23a.pdf | https://openreview.net/forum?id=DOdfxTZLyq | Individually Fair Learning with One-Sided Feedback | https://proceedings.mlr.press/v202/bechavod23a.html | Yahav Bechavod, Aaron Roth | https://proceedings.mlr.press/v202/bechavod23a.html | ICML 2023 | We consider an online learning problem with one-sided feedback, in which the learner is able to observe the true label only for positively predicted instances. On each round, $k$ instances arrive and receive classification outcomes according to a randomized policy deployed by the learner, whose goal is to maximize accu... |
https://proceedings.mlr.press/v202/becker23a.html | https://proceedings.mlr.press/v202/becker23a/becker23a.pdf | https://openreview.net/forum?id=LztkK0UZxS | Predicting Ordinary Differential Equations with Transformers | https://proceedings.mlr.press/v202/becker23a.html | Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus | https://proceedings.mlr.press/v202/becker23a.html | ICML 2023 | We develop a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy observations of a single solution trajectory. We demonstrate in extensive empirical evaluations that our model performs better or on par with existing... |
https://proceedings.mlr.press/v202/beechey23a.html | https://proceedings.mlr.press/v202/beechey23a/beechey23a.pdf | https://openreview.net/forum?id=R1blujRwj1 | Explaining Reinforcement Learning with Shapley Values | https://proceedings.mlr.press/v202/beechey23a.html | Daniel Beechey, Thomas M. S. Smith, Özgür Şimşek | https://proceedings.mlr.press/v202/beechey23a.html | ICML 2023 | For reinforcement learning systems to be widely adopted, their users must understand and trust them. We present a theoretical analysis of explaining reinforcement learning using Shapley values, following a principled approach from game theory for identifying the contribution of individual players to the outcome of a co... |
https://proceedings.mlr.press/v202/behmanesh23a.html | https://proceedings.mlr.press/v202/behmanesh23a/behmanesh23a.pdf | https://openreview.net/forum?id=PWRIIwBJFo | TIDE: Time Derivative Diffusion for Deep Learning on Graphs | https://proceedings.mlr.press/v202/behmanesh23a.html | Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov | https://proceedings.mlr.press/v202/behmanesh23a.html | ICML 2023 | A prominent paradigm for graph neural networks is based on the message-passing framework. In this framework, information communication is realized only between neighboring nodes. The challenge of approaches that use this paradigm is to ensure efficient and accurate long-distance communication between nodes, as deep con... |
https://proceedings.mlr.press/v202/benbaki23a.html | https://proceedings.mlr.press/v202/benbaki23a/benbaki23a.pdf | https://openreview.net/forum?id=RAeN6s9RZV | Fast as CHITA: Neural Network Pruning with Combinatorial Optimization | https://proceedings.mlr.press/v202/benbaki23a.html | Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder | https://proceedings.mlr.press/v202/benbaki23a.html | ICML 2023 | The sheer size of modern neural networks makes model serving a serious computational challenge. A popular class of compression techniques overcomes this challenge by pruning or sparsifying the weights of pretrained networks. While useful, these techniques often face serious tradeoffs between computational requirements ... |
https://proceedings.mlr.press/v202/bender23a.html | https://proceedings.mlr.press/v202/bender23a/bender23a.pdf | https://openreview.net/forum?id=3UHmUaOVWp | Continuously Parameterized Mixture Models | https://proceedings.mlr.press/v202/bender23a.html | Christopher M Bender, Yifeng Shi, Marc Niethammer, Junier Oliva | https://proceedings.mlr.press/v202/bender23a.html | ICML 2023 | Mixture models are universal approximators of smooth densities but are difficult to utilize in complicated datasets due to restrictions on typically available modes and challenges with initialiations. We show that by continuously parameterizing a mixture of factor analyzers using a learned ordinary differential equatio... |
https://proceedings.mlr.press/v202/bendinelli23a.html | https://proceedings.mlr.press/v202/bendinelli23a/bendinelli23a.pdf | https://openreview.net/forum?id=EiHX7MfAG0 | Controllable Neural Symbolic Regression | https://proceedings.mlr.press/v202/bendinelli23a.html | Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny | https://proceedings.mlr.press/v202/bendinelli23a.html | ICML 2023 | In symbolic regression, the objective is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants. However, the combinatorial space of possible expressions can make it challenging for traditional evolutionary algorit... |
https://proceedings.mlr.press/v202/bengs23a.html | https://proceedings.mlr.press/v202/bengs23a/bengs23a.pdf | https://openreview.net/forum?id=MUC7ASJiBT | On Second-Order Scoring Rules for Epistemic Uncertainty Quantification | https://proceedings.mlr.press/v202/bengs23a.html | Viktor Bengs, Eyke Hüllermeier, Willem Waegeman | https://proceedings.mlr.press/v202/bengs23a.html | ICML 2023 | It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture so-called aleatoric uncertainty of predictions, various machine learning methods have recently been developed with the goal to let the learn... |
https://proceedings.mlr.press/v202/bennouna23a.html | https://proceedings.mlr.press/v202/bennouna23a/bennouna23a.pdf | https://openreview.net/forum?id=4cvSExetbO | Certified Robust Neural Networks: Generalization and Corruption Resistance | https://proceedings.mlr.press/v202/bennouna23a.html | Amine Bennouna, Ryan Lucas, Bart Van Parys | https://proceedings.mlr.press/v202/bennouna23a.html | ICML 2023 | Recent work have demonstrated that robustness (to "corruption") can be at odds with generalization. Adversarial training, for instance, aims to reduce the problematic susceptibility of modern neural networks to small data perturbations. Surprisingly, overfitting is a major concern in adversarial training despite being ... |
https://proceedings.mlr.press/v202/berlinghieri23a.html | https://proceedings.mlr.press/v202/berlinghieri23a/berlinghieri23a.pdf | https://openreview.net/forum?id=Qtix8HLmDx | Gaussian processes at the Helm(holtz): A more fluid model for ocean currents | https://proceedings.mlr.press/v202/berlinghieri23a.html | Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, Tamara Broderick | https://proceedings.mlr.press/v202/berlinghieri23a.html | ICML 2023 | Oceanographers are interested in predicting ocean currents and identifying divergences in a current vector field based on sparse observations of buoy velocities. Since we expect current dynamics to be smooth but highly non-linear, Gaussian processes (GPs) offer an attractive model. But we show that applying a GP with a... |
https://proceedings.mlr.press/v202/bernasconi23a.html | https://proceedings.mlr.press/v202/bernasconi23a/bernasconi23a.pdf | https://openreview.net/forum?id=jiC1uCDIEe | Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion | https://proceedings.mlr.press/v202/bernasconi23a.html | Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Francesco Trovò, Nicola Gatti | https://proceedings.mlr.press/v202/bernasconi23a.html | ICML 2023 | Bayesian persuasion studies how an informed sender should influence beliefs of rational receivers that take decisions through Bayesian updating of a common prior. We focus on the online Bayesian persuasion framework, in which the sender repeatedly faces one or more receivers with unknown and adversarially selected type... |
https://proceedings.mlr.press/v202/bernasconi23b.html | https://proceedings.mlr.press/v202/bernasconi23b/bernasconi23b.pdf | https://openreview.net/forum?id=RgwqlatND7 | Constrained Phi-Equilibria | https://proceedings.mlr.press/v202/bernasconi23b.html | Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco Trovò, Nicola Gatti | https://proceedings.mlr.press/v202/bernasconi23b.html | ICML 2023 | The computational study of equilibria involving constraints on players’ strategies has been largely neglected. However, in real-world applications, players are usually subject to constraints ruling out the feasibility of some of their strategies, such as, e.g., safety requirements and budget caps. Computational studies... |
https://proceedings.mlr.press/v202/berrevoets23a.html | https://proceedings.mlr.press/v202/berrevoets23a/berrevoets23a.pdf | https://openreview.net/forum?id=8pCLQsEMPQ | Differentiable and Transportable Structure Learning | https://proceedings.mlr.press/v202/berrevoets23a.html | Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela Van Der Schaar | https://proceedings.mlr.press/v202/berrevoets23a.html | ICML 2023 | Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure. However, compute required to infer these structures is typically super-exponential in the number of variables, as inference requires a sweep of a combinatorially large space of potential structures. That is, u... |
https://proceedings.mlr.press/v202/berzins23a.html | https://proceedings.mlr.press/v202/berzins23a/berzins23a.pdf | https://openreview.net/forum?id=F2OjOG4j55 | Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision | https://proceedings.mlr.press/v202/berzins23a.html | Arturs Berzins | https://proceedings.mlr.press/v202/berzins23a.html | ICML 2023 | A neural network consisting of piecewise affine building blocks, such as fully-connected layers and ReLU activations, is itself a piecewise affine function supported on a polyhedral complex. This complex has been previously studied to characterize theoretical properties of neural networks, but, in practice, extracting ... |
https://proceedings.mlr.press/v202/bethune23a.html | https://proceedings.mlr.press/v202/bethune23a/bethune23a.pdf | https://openreview.net/forum?id=g68Q7mL0P5 | Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks | https://proceedings.mlr.press/v202/bethune23a.html | Louis Béthune, Paul Novello, Guillaume Coiffier, Thibaut Boissin, Mathieu Serrurier, Quentin Vincenot, Andres Troya-Galvis | https://proceedings.mlr.press/v202/bethune23a.html | ICML 2023 | We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the boundary of the support of any distribution. The distance to the support can be interpreted as a normality score, and its approximation usi... |
https://proceedings.mlr.press/v202/bevilacqua23a.html | https://proceedings.mlr.press/v202/bevilacqua23a/bevilacqua23a.pdf | https://openreview.net/forum?id=kP2p67F4G7 | Neural Algorithmic Reasoning with Causal Regularisation | https://proceedings.mlr.press/v202/bevilacqua23a.html | Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Veličković | https://proceedings.mlr.press/v202/bevilacqua23a.html | ICML 2023 | Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of neural networks, effectively demonstrating they can learn to execute classical algorithms on unseen data coming from the train distribution. However, the performance of existing neural reasoners significantly degrades on out-of-d... |
https://proceedings.mlr.press/v202/bharti23a.html | https://proceedings.mlr.press/v202/bharti23a/bharti23a.pdf | https://openreview.net/forum?id=s4dX9ymHrP | Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference | https://proceedings.mlr.press/v202/bharti23a.html | Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, Francois-Xavier Briol | https://proceedings.mlr.press/v202/bharti23a.html | ICML 2023 | Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum mean discrepancy (MMD), which has previously been used for approximate Bayesian computation, minimum distance estimation, generalised Bayesian inference, and within the nonparametric learn... |
https://proceedings.mlr.press/v202/bhaskara23a.html | https://proceedings.mlr.press/v202/bhaskara23a/bhaskara23a.pdf | https://openreview.net/forum?id=SgeIqUvo4w | Bandit Online Linear Optimization with Hints and Queries | https://proceedings.mlr.press/v202/bhaskara23a.html | Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit | https://proceedings.mlr.press/v202/bhaskara23a.html | ICML 2023 | We study variants of the online linear optimization (OLO) problem with bandit feedback, where the algorithm has access to external information about the unknown cost vector. Our motivation is the recent body of work on using such “hints” towards improving regret bounds for OLO problems in the full-information setting. ... |
https://proceedings.mlr.press/v202/bhatnagar23a.html | https://proceedings.mlr.press/v202/bhatnagar23a/bhatnagar23a.pdf | https://openreview.net/forum?id=qqMcym6AmS | Improved Online Conformal Prediction via Strongly Adaptive Online Learning | https://proceedings.mlr.press/v202/bhatnagar23a.html | Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai | https://proceedings.mlr.press/v202/bhatnagar23a.html | ICML 2023 | We study the problem of uncertainty quantification via prediction sets, in an online setting where the data distribution may vary arbitrarily over time. Recent work develops online conformal prediction techniques that leverage regret minimization algorithms from the online learning literature to learn prediction sets w... |
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ICML 2023 International Conference on Machine Learning 2023 Accepted Paper Meta Info Dataset
This dataset is collect from the ICML 2024 OpenReview website (https://openreview.net/group?id=ICML.cc/2023/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/icml2023). For researchers who are interested in doing analysis of ICML 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 ICML 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
{
"abs": "https://proceedings.mlr.press/v202/aamand23a.html",
"Download PDF": "https://proceedings.mlr.press/v202/aamand23a/aamand23a.pdf",
"OpenReview": "https://openreview.net/forum?id=BVomXLJQoH",
"title": "Data Structures for Density Estimation",
"url": "https://proceedings.mlr.press/v202/aamand23a.html",
"authors": "Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal",
"detail_url": "https://proceedings.mlr.press/v202/aamand23a.html",
"tags": "ICML 2023",
"abstract": "We study statistical/computational tradeoffs for the following density estimation problem: given $k$ distributions $v_1, \\ldots, v_k$ over a discrete domain of size $n$, and sampling access to a distribution $p$, identify $v_i$ that is \"close\" to $p$. Our main result is the first data structure that, given a sublinear (in $n$) number of samples from $p$, identifies $v_i$ in time sublinear in $k$. We also give an improved version of the algorithm of Acharya et al. (2018) that reports $v_i$ in time linear in $k$. The experimental evaluation of the latter algorithm shows that it achieves a significant reduction in the number of operations needed to achieve a given accuracy compared to prior work."
}
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