ICML 2023

1,828 papers

Variational Mixture of HyperGenerators for Learning Distributions over Functions

Batuhan Koyuncu, Pablo Sanchez Martin, Ignacio Peis, Pablo M. Olmos, Isabel Valera

Benign Overfitting in Two-layer ReLU Convolutional Neural Networks

Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu

Randomized Schur Complement Views for Graph Contrastive Learning

Vignesh Kothapalli

TabDDPM: Modelling Tabular Data with Diffusion Models

Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, Artem Babenko

Ewald-based Long-Range Message Passing for Molecular Graphs

Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann

Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions

Ezgi Korkmaz, Jonah Brown-Cohen

Pretraining Language Models with Human Preferences

Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher Buckley

Parameter-Level Soft-Masking for Continual Learning

Tatsuya Konishi, Mori Kurokawa, Chihiro Ono, Zixuan Ke, Gyuhak Kim

Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference

Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek

Covariate balancing using the integral probability metric for causal inference

Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim

End-to-End Full-Atom Antibody Design

Xiangzhe Kong, Wenbing Huang, Yang Liu

Autoregressive Diffusion Model for Graph Generation

Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash

GOAT: A Global Transformer on Large-scale Graphs

Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss

On Computing Optimal Tree Ensembles

Christian Komusiewicz, Pascal Kunz, Frank Sommer, Manuel Sorge

Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees

Anastasia Koloskova, Hadrien Hendrikx, Sebastian U Stich

Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets

Nils Kohring, Fabian Raoul Pieroth, Martin Bichler

Rigid Body Flows for Sampling Molecular Crystal Structures

Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noe

Grounding Language Models to Images for Multimodal Inputs and Outputs

Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried

Online Learning with Feedback Graphs: The True Shape of Regret

Tomáš Kocák, Alexandra Carpentier

Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?

Boris Knyazev, Doha Hwang, Simon Lacoste-Julien

Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost

Marina Knittel, Max Springer, John P Dickerson, MohammadTaghi Hajiaghayi

Deep Laplacian-based Options for Temporally-Extended Exploration

Martin Klissarov, Marlos C. Machado

Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions

Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M Morris

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice

Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko

IncDSI: Incrementally Updatable Document Retrieval

Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, Kilian Q Weinberger

Training Normalizing Flows from Dependent Data

Matthias Kirchler, Christoph Lippert, Marius Kloft

Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs

Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh

A Watermark for Large Language Models

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers

Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network

Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda

Proper Losses for Discrete Generative Models

Dhamma Kimpara, Rafael Frongillo, Bo Waggoner

Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning

Kwangho Kim, Jose R Zubizarreta

Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs

Dale Kim, Qing Zhou

Denoising MCMC for Accelerating Diffusion-Based Generative Models

Beomsu Kim, Jong Chul Ye

Unifying Nesterov’s Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions

Jungbin Kim, Insoon Yang

Learnability and Algorithm for Continual Learning

Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Bing Liu

Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference

Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner

An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning

Woojun Kim, Youngchul Sung

Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning

Jaehyung Kim, Jinwoo Shin, Dongyeop Kang

Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning

Seungwook Kim, Chunghyun Park, Yoonwoo Jeong, Jaesik Park, Minsu Cho

SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes

Junetae Kim, Kyoungsuk Park, Hanseok Jeong, Youngwook Kim, Jeongseon Kim

Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization

Chanyeong Kim, Jongwoong Park, Hyunglip Bae, Woo Chang Kim

Model-based Offline Reinforcement Learning with Count-based Conservatism

Byeongchan Kim, Min-hwan Oh

Regularizing Towards Soft Equivariance Under Mixed Symmetries

Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee

Margin-based Neural Network Watermarking

Byungjoo Kim, Suyoung Lee, Seanie Lee, Sooel Son, Sung Ju Hwang

Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills

Seongun Kim, Kyowoon Lee, Jaesik Choi

Probabilistic Imputation for Time-series Classification with Missing Data

SeungHyun Kim, Hyunsu Kim, Eunggu Yun, Hwangrae Lee, Jaehun Lee

BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models

Taebum Kim, Hyoungjoo Kim, Gyeong-In Yu, Byung-gon Chun

LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

Woojun Kim, Jeonghye Kim, Youngchul Sung

Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning

Junghoon Kim, Taejoon Kim, David Love, Christopher Brinton

Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, Il-chul Moon

DevFormer: A Symmetric Transformer for Context-Aware Device Placement

Haeyeon Kim, Minsu Kim, Federico Berto, joungho kim, Jinkyoo Park

Probabilistic Concept Bottleneck Models

Eunji Kim, Dahuin Jung, Sangha Park, Siwon Kim, Sungroh Yoon

Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming

Jinuk Kim, Yeonwoo Jeong, Deokjae Lee, Hyun Oh Song

Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback

Wonyoung Kim, Garud Iyengar, Assaf Zeevi

Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum

Jigang Kim, Daesol Cho, H. Jin Kim

SAAL: Sharpness-Aware Active Learning

Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim

Trainability, Expressivity and Interpretability in Gated Neural ODEs

Timothy Doyeon Kim, Tankut Can, Kamesh Krishnamurthy

CrossSplit: Mitigating Label Noise Memorization through Data Splitting

Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien

Learning-augmented private algorithms for multiple quantile release

Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii

Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions

Mahyar Khayatkhoei, Wael AbdAlmageed

Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach

Prashant Khanduri, Ioannis Tsaknakis, Yihua Zhang, Jia Liu, Sijia Liu

Loss Balancing for Fair Supervised Learning

Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan

Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems

Mohammad Khalafi, Digvijay Boob

PAC Prediction Sets for Large Language Models of Code

Adam Khakhar, Stephen Mell, Osbert Bastani

Rethinking Backdoor Attacks

Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman

Homomorphism AutoEncoder -- Learning Group Structured Representations from Observed Transitions

Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F Grewe, Bernhard Schölkopf

Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks

T. Anderson Keller, Max Welling

Exact Inference in High-order Structured Prediction

Chuyang Ke, Jean Honorio

The Test of Tests: A Framework for Differentially Private Hypothesis Testing

Zeki Kazan, Kaiyan Shi, Adam Groce, Andrew P Bray

Instrumental Variable Estimation of Average Partial Causal Effects

Yuta Kawakami, manabu kuroki, Jin Tian

How Does Information Bottleneck Help Deep Learning?

Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang

Curious Replay for Model-based Adaptation

Isaac Kauvar, Chris Doyle, Linqi Zhou, Nick Haber

Learning Mixtures of Markov Chains and MDPs

Chinmaya Kausik, Kevin Tan, Ambuj Tewari

Multi-Modal Classifiers for Open-Vocabulary Object Detection

Prannay Kaul, Weidi Xie, Andrew Zisserman

Data Representations’ Study of Latent Image Manifolds

Ilya Kaufman, Omri Azencot

Regression with Sensor Data Containing Incomplete Observations

Takayuki Katsuki, Takayuki Osogami

General Sequential Episodic Memory Model

Arjun Karuvally, Terrence Sejnowski, Hava T Siegelmann

Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis

Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh

On the Relationship Between Explanation and Prediction: A Causal View

Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim

Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning

Amin Karbasi, Nikki Lijing Kuang, Yian Ma, Siddharth Mitra

Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias

Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa

Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks

Qiyu Kang, KAI ZHAO, Yang Song, Sijie Wang, Wee Peng Tay

Poisoning Generative Replay in Continual Learning to Promote Forgetting

Siteng Kang, Zhan Shi, Xinhua Zhang

Beyond Reward: Offline Preference-guided Policy Optimization

Yachen Kang, Diyuan Shi, Jinxin Liu, LI He, Donglin Wang

Leveraging Proxy of Training Data for Test-Time Adaptation

Juwon Kang, Nayeong Kim, Donghyeon Kwon, Jungseul Ok, Suha Kwak

A Deep Conjugate Direction Method for Iteratively Solving Linear Systems

Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Alicia Trevino Kala, David Hyde

Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models

Nikhil Kandpal, Brian Lester, Mohammed Muqeeth, Anisha Mascarenhas, Monty Evans

Large Language Models Struggle to Learn Long-Tail Knowledge

Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel

One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training

Sekitoshi Kanai, Shin’ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi, Kentaro Ohno

Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search

Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin

Nonlinear Causal Discovery with Latent Confounders

David Kaltenpoth, Jilles Vreeken

Identifying Interpretable Subspaces in Image Representations

Neha Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi

Statistical Indistinguishability of Learning Algorithms

Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas

Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies

Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami

Equivariance with Learned Canonicalization Functions

Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh

The Catalog Problem: Clustering and Ordering Variable-Sized Sets

Mateusz Maria Jurewicz, Graham W. Taylor, Leon Derczynski

Estimating Joint Treatment Effects by Combining Multiple Experiments

Yonghan Jung, Jin Tian, Elias Bareinboim

Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation

Yeonsung Jung, Hajin Shim, June Yong Yang, Eunho Yang

Scaling of Class-wise Training Losses for Post-hoc Calibration

Seungjin Jung, Seungmo Seo, Yonghyun Jeong, Jongwon Choi

FARE: Provably Fair Representation Learning with Practical Certificates

Nikola Jovanović, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin Vechev