AISTATS

Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning

Chihyeon Song, Jaewoo Lee, Jinkyoo Park

Demystifying Transition Matching: When and Why It Can Beat Flow Matching

Jaihoon Kim, Rajarshi Saha, Minhyuk Sung, Youngsuk Park

GL-LowPopArt: A Nearly Instance-Wise Minimax Estimator for (Adaptive) Generalized Linear Low-Rank Trace Regression

Junghyun Lee, Kyoungseok Jang, Kwang-Sung Jun, Milan Vojnovic, Se-Young Yun

GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression

Junghyun Lee, Kyoungseok Jang, Kwang-Sung Jun, Milan Vojnovic, Se-Young Yun

Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning

Seungyub Han, Hyung Jjn Kim, and Jungwoo Lee

Near-Optimal Clustering in Mixture of Markov Chains

Junghyun Lee, Yassir Jedra, Alexandre Proutière, Se-Young Yun

Towards Motion-aware Referring Image Segmentation

Chaeyun Kim, Seunghoon Yi, Yejin Kim, Yohan Jo, Joonseok Lee

Towards Motion-aware Referring Image Segmentation

Chaeyun Kim, Seunghoon Yi, Yejin Kim, Yohan Jo, Joonseok Lee

Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling

Himchan Hwang*, Hyeokju Jeong*, Dong Kyu Shin*, Che-Sang Park, Sehee Kweon, Sangwoong Yoon, Frank C. Park

A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning

C. Lee, J. Oh, K. Lee, J. Sohn

A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning

Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn

Ant Colony Sampling with GFlowNets for Combinatorial Optimization

Minsu Kim*, Sanghyeok Choi*, Hyeonah Kim, Jiwoo Son, Jinkyoo Park, Yoshua Bengio

Bayesian Principles Improve Prompt Learning in Vision-Language Models

Mingyu Kim, Jongwoo Ko, Mi-Jung Park

HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning

Tuan Ngo Nguyen, Jay Barrett, Kwang-Sung Jun

Minimum Empirical Divergence for Sub-Gaussian Linear Bandits

Kapilan Balagopalan, Kwang-Sung Jun

Optimal estimation of linear non-Gaussian structure equation models

Oh, Sunmin, Seungsu Han, and Gunwoong Park

Analysis of Using Sigmoid Loss for Contrastive Learning

C. Lee, J. Chang, J. Sohn

Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models

Soheun Yi, Sanghack Lee *

Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion

Junghyun Lee, Se-Young Yun, Kwang-Sung Jun

Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion

Junghyun Lee, Se-Young Yun, Kwang-Sung Jun

Pathwise Explanation of ReLU Neural Networks

Seongwoo Lim, Won Jo, Joohyung Lee, Jaesik Choi

Towards a Complete Benchmark on Video Moment Localization

Jinyeong Chae, Donghwa Kim, Kwanseok Kim, Doyeon Lee, Sangho Lee, Seongsu Ha, Jonghwan Mun, Wooyoung Kang, Byungseok Roh, Joonseok Lee

Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior

Yohan Jung and Jinkyoo Park

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

Jung-hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin, Richard Combes

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

Jung-hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin, Richard Combes

Loss Curvature Matching for Dataset Selection and Condensation

Seungjae Shin, Heesun Bae, DongHyeok Shin, Weonyoung Joo, and Il-Chul Moon

Nearly Optimal Latent State Decoding in Block MDPs

Yassir Jedra, Junghyun Lee, Alexandre Proutière, Se-Young Yun

Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits

Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh

T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression

Yuchao Qin, Mihaela van der Schaar, Changhee Lee