ICML 2023

1,828 papers

Overcoming Simplicity Bias in Deep Networks using a Feature Sieve

Rishabh Tiwari, Pradeep Shenoy

Perturbation Analysis of Neural Collapse

Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed

PCA-based Multi-Task Learning: a Random Matrix Approach

Malik Tiomoko, Romain Couillet, Frederic Pascal

Differentiable Multi-Target Causal Bayesian Experimental Design

Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal

Margin-based sampling in high dimensions: When being active is less efficient than staying passive

Alexandru Tifrea, Jacob Clarysse, Fanny Yang

Fast Rates for Maximum Entropy Exploration

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos

Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes

Louis C. Tiao, Vincent Dutordoir, Victor Picheny

ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models

Qinglong Tian, Xin Zhang, Jiwei Zhao

Target-Aware Generative Augmentations for Single-Shot Adaptation

Kowshik Thopalli, Rakshith Subramanyam, Pavan K. Turaga, Jayaraman J. Thiagarajan

On the Estimation of Gaussian Mixture Copula Models

Ashutosh Tewari

PWSHAP: A Path-Wise Explanation Model for Targeted Variables

Lucile Ter-Minassian, Oscar Clivio, Karla DiazOrdaz, Robin J. Evans, Christopher C. Holmes

Reinforcement Learning with History Dependent Dynamic Contexts

Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier

Finding Generalization Measures by Contrasting Signal and Noise

Jiaye Teng, Bohang Zhang, Ruichen Li, Haowei He, Yequan Wang

Concurrent Shuffle Differential Privacy Under Continual Observation

Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control

Jacopo Teneggi, Matthew Tivnan, Web Stayman, Jeremias Sulam

Deep Regression Unlearning

Ayush Kumar Tarun, Vikram Singh Chundawat, Murari Mandal, Mohan Kankanhalli

Data Feedback Loops: Model-driven Amplification of Dataset Biases

Rohan Taori, Tatsunori Hashimoto

Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization

Stone Tao, Xiaochen Li, Tongzhou Mu, Zhiao Huang, Yuzhe Qin

Dual Focal Loss for Calibration

Linwei Tao, Minjing Dong, Chang Xu

POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models

Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He, Mingyuan Zhou

End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization

Shohei Taniguchi, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo

Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding

Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Longfei Li

Defects of Convolutional Decoder Networks in Frequency Representation

Ling Tang, Wen Shen, Zhanpeng Zhou, YueFeng Chen, Quanshi Zhang

VA-learning as a more efficient alternative to Q-learning

Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko

Towards a better understanding of representation dynamics under TD-learning

Yunhao Tang, Rémi Munos

Towards Understanding Generalization of Graph Neural Networks

Huayi Tang, Yong Liu

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm

Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos

Understanding Self-Predictive Learning for Reinforcement Learning

Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Avila Pires, Yash Chandak

From Perception to Programs: Regularize, Overparameterize, and Amortize

Hao Tang, Kevin Ellis

Regret-Minimizing Double Oracle for Extensive-Form Games

Xiaohang Tang, Le Cong Dinh, Stephen Marcus McAleer, Yaodong Yang

Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois

Provably Invariant Learning without Domain Information

Xiaoyu Tan, LIN Yong, Shengyu Zhu, Chao Qu, Xihe Qiu

Robust Explanation for Free or At the Cost of Faithfulness

Zeren Tan, Yang Tian

Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes

Shion Takeno, Masahiro Nomura, Masayuki Karasuyama

Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds

Shion Takeno, Yu Inatsu, Masayuki Karasuyama

Contextual Conservative Interleaving Bandits

Kei Takemura

Learning Neural PDE Solvers with Parameter-Guided Channel Attention

Makoto Takamoto, Francesco Alesiani, Mathias Niepert

Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input

Shokichi Takakura, Taiji Suzuki

Learning Mixtures of Gaussians with Censored Data

Wai Ming Tai, Bryon Aragam

MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods

Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott MacLachlan, Luke Olson

Adaptive Coordination in Social Embodied Rearrangement

Andrew Szot, Unnat Jain, Dhruv Batra, Zsolt Kira, Ruta Desai

Optimal randomized multilevel Monte Carlo for repeatedly nested expectations

Yasa Syed, Guanyang Wang

Von Mises Mixture Distributions for Molecular Conformation Generation

Kirk Swanson, Jake Lawrence Williams, Eric M Jonas

Inverse Reinforcement Learning without Reinforcement Learning

Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Steven Wu

Proximal Causal Learning of Conditional Average Treatment Effects

Erik Sverdrup, Yifan Cui

Tight and fast generalization error bound of graph embedding in metric space

Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian

Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic

Wesley A Suttle, Amrit Bedi, Bhrij Patel, Brian M. Sadler, Alec Koppel

Tuning Computer Vision Models With Task Rewards

André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, Xiaohua Zhai

When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction

Vinith Menon Suriyakumar, Marzyeh Ghassemi, Berk Ustun

Inflow, Outflow, and Reciprocity in Machine Learning

Mukund Sundararajan, Walid Krichene

Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning

Yihao Sun, Jiaji Zhang, Chengxing Jia, Haoxin Lin, Junyin Ye

Feature Expansion for Graph Neural Networks

Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng Xu, Kun Zhang

A Neural PDE Solver with Temporal Stencil Modeling

Zhiqing Sun, Yiming Yang, Shinjae Yoo

SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation

Shikun Sun, Longhui Wei, Junliang Xing, Jia Jia, Qi Tian

A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification

Jiachen Sun, Jiongxiao Wang, Weili Nie, Zhiding Yu, Zhuoqing Mao

Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions

Tao Sun, Qingsong Wang, Dongsheng Li, Bao Wang

All in a Row: Compressed Convolution Networks for Graphs

Junshu Sun, Shuhui Wang, Xinzhe Han, Zhe Xue, Qingming Huang

Learning Prescriptive ReLU Networks

Wei Sun, Asterios Tsiourvas

When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis

Yiyou Sun, Zhenmei Shi, Yingyu Liang, Yixuan Li

Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape

Yan Sun, Li Shen, Shixiang Chen, Liang Ding, Dacheng Tao

MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior

Jennifer J. Sun, Markus Marks, Andrew Wesley Ulmer, Dipam Chakraborty, Brian Geuther

Tensor Gaussian Process with Contraction for Multi-Channel Imaging Analysis

Hu Sun, Ward Manchester, Meng Jin, Yang Liu, Yang Chen

Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming

Chunlin Sun, Shang Liu, Xiaocheng Li

What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings

Zequn Sun, Jiacheng Huang, Xiaozhou Xu, Qijin Chen, Weijun Ren

Revisiting Sampling for Combinatorial Optimization

Haoran Sun, Katayoon Goshvadi, Azade Nova, Dale Schuurmans, Hanjun Dai

MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks

Wenfang Sun, Yingjun Du, Xiantong Zhen, Fan Wang, Ling Wang

Vector-Valued Control Variates

Zhuo Sun, Alessandro Barp, François-Xavier Briol

Distilling Internet-Scale Vision-Language Models into Embodied Agents

Theodore Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, Ishita Dasgupta

Adversarial Learning of Distributional Reinforcement Learning

Yang Sui, Yukun Huang, Hongtu Zhu, Fan Zhou

Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels

Min-Kook Suh, Seung-Woo Seo

DUET: 2D Structured and Approximately Equivariant Representations

Xavier Suau, Federico Danieli, T. Anderson Keller, Arno Blaas, Chen Huang

Towards Robust Graph Incremental Learning on Evolving Graphs

Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu

Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes

Nico Stucki, Johannes C. Paetzold, Suprosanna Shit, Bjoern Menze, Ulrich Bauer

Kernel QuantTree

Diego Stucchi, Paolo Rizzo, Nicolò Folloni, Giacomo Boracchi

Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers

Grant Strimel, Yi Xie, Brian John King, Martin Radfar, Ariya Rastrow

Improving Expert Predictions with Conformal Prediction

Eleni Straitouri, Lequn Wang, Nastaran Okati, Manuel Gomez Rodriguez

MODeL: Memory Optimizations for Deep Learning

Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty

Partial Optimality in Cubic Correlation Clustering

David Stein, Silvia Di Gregorio, Bjoern Andres

FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels

Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau

Generating Language Corrections for Teaching Physical Control Tasks

Megha Srivastava, Noah Goodman, Dorsa Sadigh

Linear Causal Disentanglement via Interventions

Chandler Squires, Anna Seigal, Salil S Bhate, Caroline Uhler

Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism.

Aude Sportisse, Hugo Schmutz, Olivier HUMBERT, Charles Bouveyron, Pierre-Alexandre Mattei

Differentiable Tree Operations Promote Compositional Generalization

Paul Soulos, Edward J Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez

Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation

Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu

A Nearly-Optimal Bound for Fast Regression with ℓ_∞ Guarantee

Zhao Song, Mingquan Ye, Junze Yin, Lichen Zhang

Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance

Zhao Song, Xin Yang, Yuanyuan Yang, Lichen Zhang

Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability

Zhao Song, Yitan Wang, zheng Yu, Lichen Zhang

Importance Weighted Expectation-Maximization for Protein Sequence Design

Zhenqiao Song, Lei Li

RGE: A Repulsive Graph Rectification for Node Classification via Influence

Jaeyun Song, SungYub Kim, Eunho Yang

FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks

Bingqing Song, Prashant Khanduri, Xinwei Zhang, Jinfeng Yi, Mingyi Hong

Latent Traversals in Generative Models as Potential Flows

Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling

Deep Perturbation Learning: Enhancing the Network Performance via Image Perturbations

Zifan Song, Xiao Gong, Guosheng Hu, Cairong Zhao

LipsNet: A Smooth and Robust Neural Network with Adaptive Lipschitz Constant for High Accuracy Optimal Control

Xujie Song, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Chen Chen

Consistency Models

Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever

Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization

Jiwoo Son, Minsu Kim, Hyeonah Kim, Jinkyoo Park

Abstracting Imperfect Information Away from Two-Player Zero-Sum Games

Samuel Sokota, Ryan D’Orazio, Chun Kai Ling, David J Wu, J Zico Kolter

The Dormant Neuron Phenomenon in Deep Reinforcement Learning

Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci

Fair Densities via Boosting the Sufficient Statistics of Exponential Families

Alexander Soen, Hisham Husain, Richard Nock

On the Effectiveness of Offline RL for Dialogue Response Generation

Paloma Sodhi, Felix Wu, Ethan R. Elenberg, Kilian Q Weinberger, Ryan Mcdonald

A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems

Oliver Slumbers, David Henry Mguni, Stefano B Blumberg, Stephen Marcus McAleer, Yaodong Yang