AISTATS 2024

547 papers

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

Dongxia Wu, Tsuyoshi Ide, Georgios Kollias, Jiri Navratil, Aurélie Lozano

Robust Non-linear Normalization of Heterogeneous Feature Distributions with Adaptive Tanh-Estimators

Felip Guimerà Cuevas, Helmut Schmid

Error bounds for any regression model using Gaussian processes with gradient information

Rafael Savvides, Hoang Phuc Hau Luu, Kai Puolamäki

Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication

Hugo Richard, Etienne Boursier, Vianney Perchet

Multi-armed bandits with guaranteed revenue per arm

Dorian Baudry, Nadav Merlis, Mathieu Benjamin Molina, Hugo Richard, Vianney Perchet

Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers

Yuya Yoshikawa, Tomoharu Iwata

TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation

Chen Li, Yoshihiro Yamanishi

Optimal Sparse Survival Trees

Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin

Emergent specialization from participation dynamics and multi-learner retraining

Sarah Dean, Mihaela Curmei, Lillian Ratliff, Jamie Morgenstern, Maryam Fazel

Pure Exploration in Bandits with Linear Constraints

Emil Carlsson, Debabrota Basu, Fredrik Johansson, Devdatt Dubhashi

Fast 1-Wasserstein distance approximations using greedy strategies

Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada

DNNLasso: Scalable Graph Learning for Matrix-Variate Data

Meixia LIN, Yangjing Zhang

Enhancing In-context Learning via Linear Probe Calibration

Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz

Breaking isometric ties and introducing priors in Gromov-Wasserstein distances

Pinar Demetci, Quang Huy TRAN, Ievgen Redko, Ritambhara Singh

On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation

Jiawei Huang, Batuhan Yardim, Niao He

A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning

Kihyuk Hong, Yuhang Li, Ambuj Tewari

HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model via Self-Supervised Learning

Zhenyu Zhang, Jiudong Yang

Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes

Haoming Yang, Ali Hasan, Yuting Ng, Vahid Tarokh

Best Arm Identification with Resource Constraints

Zitian Li, Wang Chi Cheung

Fast Dynamic Sampling for Determinantal Point Processes

Zhao Song, Junze Yin, Lichen Zhang, Ruizhe Zhang

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?

Kyurae Kim, Yian Ma, Jacob Gardner

Identifying Copeland Winners in Dueling Bandits with Indifferences

Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier

Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations

Hanjiang Hu, ZUXIN LIU, Linyi Li, Jiacheng Zhu, Ding Zhao

Solving Attention Kernel Regression Problem via Pre-conditioner

Zhao Song, Junze Yin, Lichen Zhang

Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective

Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng

An Online Bootstrap for Time Series

Nicolai Palm, Thomas Nagler

Is this model reliable for everyone? Testing for strong calibration

Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene A Pennello

Double InfoGAN for Contrastive Analysis

Florence Carton, Robin Louiset, Pietro Gori

Density Uncertainty Layers for Reliable Uncertainty Estimation

Yookoon Park, David Blei

Local Causal Discovery with Linear non-Gaussian Cyclic Models

Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang

A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport

Tianyi Lin, Marco Cuturi, Michael Jordan

Simple and scalable algorithms for cluster-aware precision medicine

Amanda M. Buch, Conor Liston, Logan Grosenick

Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors

Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe

Ordinal Potential-based Player Rating

Nelson Vadori, Rahul Savani

Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels

Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d’Alché-Buc

Distributionally Robust Model-based Reinforcement Learning with Large State Spaces

Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic

Better Batch for Deep Probabilistic Time Series Forecasting

Zhihao Zheng, Seongjin Choi, Lijun Sun

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization

Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin

Comparing Comparators in Generalization Bounds

Fredrik Hellström, Benjamin Guedj

Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization

Mathieu Even, Anastasia Koloskova, Laurent Massoulié

Boundary-Aware Uncertainty for Feature Attribution Explainers

Davin Hill, Aria Masoomi, Max Torop, Sandesh Ghimire, Jennifer Dy

Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning

Zhishuai Li, Yunhao Nie, Ziyue Li, LEI BAI, Yisheng Lv

Personalized Federated X-armed Bandit

Wenjie Li, Qifan Song, Jean Honorio

Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning

Taeuk Jang, Hongchang Gao, Pengyi Shi, Xiaoqian Wang

A/B testing under Interference with Partial Network Information

Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau

Fair k-center Clustering with Outliers

Daichi Amagata

Scalable Higher-Order Tensor Product Spline Models

David Ruegamer