UAI 2023

243 papers

Regularized online DR-submodular optimization

Pengyu Zuo, Yao Wang, Shaojie Tang

MixupE: Understanding and improving Mixup from directional derivative perspective

Yingtian Zou, Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham

Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter

Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang

AUC Maximization in Imbalanced Lifelong Learning

Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu

Convergence rates for localized actor-critic in networked Markov potential games

Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman

Learning robust representation for reinforcement learning with distractions by reward sequence prediction

Qi Zhou, Jie Wang, Qiyuan Liu, Yufei Kuang, Wengang Zhou

RDM-DC: Poisoning Resilient Dataset Condensation with Robust Distribution Matching

Tianhang Zheng, Baochun Li

Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning

Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran

Conditional counterfactual causal effect for individual attribution

Ruiqi Zhao, Lei Zhang, Shengyu Zhu, Zitong Lu, Zhenhua Dong

Greed is good: correspondence recovery for unlabeled linear regression

Hang Zhang, Ping Li

Graph Self-supervised Learning via Proximity Distribution Minimization

Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava

Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL

W. Zhang, J. He, D. Zhou, Q. Gu, A. Zhang

Energy-based Predictive Representations for Partially Observed Reinforcement Learning

Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez

Fast Teammate Adaptation in the Presence of Sudden Policy Change

Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue, Chengxing Jia

Online estimation of similarity matrices with incomplete data

Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li

Monte-Carlo Search for an Equilibrium in Dec-POMDPs

Yang You, Vincent Thomas, Francis Colas, Olivier Buffet

Towards Physically Reliable Molecular Representation Learning

Seunghoon Yi, Youngwoo Cho, Jinhwan Sul, Seung Woo Ko, Soo Kyung Kim

Mitigating Transformer Overconfidence via Lipschitz Regularization

Wenqian Ye, Yunsheng Ma, Xu Cao, Kun Tang

MMEL: A Joint Learning Framework for Multi-Mention Entity Linking

Chengmei Yang, Bowei He, Yimeng Wu, Chao Xing, Lianghua He

Multi-modal differentiable unsupervised feature selection

Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe

Mixture of Normalizing Flows for European Option Pricing

Yongxin Yang, Timothy M. Hospedales

Pessimistic Model Selection for Offline Deep Reinforcement Learning

Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, Pin-Yu Chen

Provably Efficient Adversarial Imitation Learning with Unknown Transitions

Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo

$E(2)$-Equivariant Vision Transformer

Renjun Xu, Kaifan Yang, Ke Liu, Fengxiang He

Conformal Risk Control for Ordinal Classification

Yunpeng Xu, Wenge Guo, Zhi Wei

Two-stage holistic and contrastive explanation of image classification

Weiyan Xie, Xiao-Hui Li, Zhi Lin, Leonard K. M. Poon, Caleb Chen Cao

A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization

Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi

Robust Quickest Change Detection for Unnormalized Models

Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh

Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension

Yue Wu, Jiafan He, Quanquan Gu

Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning

Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger

Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?

Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier

Bidirectional Attention as a Mixture of Continuous Word Experts

Kevin C. Wibisono, Yixin Wang

On the Role of Generalization in Transferability of Adversarial Examples

Yilin Wang, Farzan Farnia

A constrained Bayesian approach to out-of-distribution prediction

Ziyu Wang, Binjie Yuan, Jiaxun Lu, Bowen Ding, Yunfeng Shao

Robust distillation for worst-class performance: on the interplay between teacher and student objectives

Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, MICHAL LUKASIK

A trajectory is worth three sentences: multimodal transformer for offline reinforcement learning

Yiqi Wang, Mengdi Xu, Laixi Shi, Yuejie Chi

Diversity-enhanced probabilistic ensemble for uncertainty estimation

Hanjing Wang, Qiang Ji

Efficient Privacy-Preserving Stochastic Nonconvex Optimization

Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu

Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits?

Xuchuang Wang, Lin Yang, Yu-Zhen Janice Chen, Xutong Liu, Mohammad Hajiesmaili

Birds of an odd feather: guaranteed out-of-distribution (OOD) novel category detection

Yoav Wald, Suchi Saria

A policy gradient approach for optimization of smooth risk measures

Nithia Vijayan, L. A. Prashanth

Probabilistic circuits that know what they don’t know

Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting

Bandits with costly reward observations

Aaron D. Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims

SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models

Vithursan Thangarasa, Abhay Gupta, William Marshall, Tianda Li, Kevin Leong

Fairness-aware class imbalanced learning on multiple subgroups

Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, Li Shen

Low-rank matrix recovery with unknown correspondence

Zhiwei Tang, Tsung-Hui Chang, Xiaojing Ye, Hongyuan Zha

Two-stage Kernel Bayesian Optimization in High Dimensions

Jian Tan, Niv Nayman

Exploiting Inferential Structure in Neural Processes

Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric Nalisnick

Why Out-of-Distribution detection experiments are not reliable - subtle experimental details muddle the OOD detector rankings

Kamil Szyc, Tomasz Walkowiak, Henryk Maciejewski

Locally Regularized Sparse Graph by Fast Proximal Gradient Descent

Dongfang Sun, Yingzhen Yang

Pandering in a (flexible) representative democracy

Xiaolin Sun, Jacob Masur, Ben Abramowitz, Nicholas Mattei, Zizhan Zheng

Meta-learning Control Variates: Variance Reduction with Limited Data

Zhuo Sun, Chris J Oates, François-Xavier Briol

On the informativeness of supervision signals

Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua Peterson

Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited

Jinyan Su, Changhong Zhao, Di Wang

Solving multi-model MDPs by coordinate ascent and dynamic programming

Xihong Su, Marek Petrik

Fast Heterogeneous Federated Learning with Hybrid Client Selection

Duanxiao Song, Guangyuan Shen, Dehong Gao, Libin Yang, Xukai Zhou

ViBid: Linear Vision Transformer with Bidirectional Normalization

Jeonggeun Song, Heung-Chang Lee

Aligned Diffusion Schrödinger Bridges

Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, María Rodríguez Martínez, Andreas Krause

On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks

Joar Skalse, Alessandro Abate

Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference

Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan

A Bayesian approach for bandit online optimization with switching cost

Zai Shi, Jian Tan, Feifei Li

Learning Nonlinear Causal Effect via Kernel Anchor Regression

Wenqi Shi, Wenkai Xu

Risk-limiting financial audits via weighted sampling without replacement

Shubhanshu Shekhar, Ziyu Xu, Zachary Lipton, Pierre Liang, Aaditya Ramdas

SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs

Vishal Sharma, Daman Arora, Mausam, Parag Singla

Counting Background Knowledge Consistent Markov Equivalent Directed Acyclic Graphs

Vidya Sagar Sharma

Efficiently learning the graph for semi-supervised learning

Dravyansh Sharma, Maxwell Jones

Implicit Training of Inference Network Models for Structured Prediction

Shiv Shankar

Mnemonist: Locating Model Parameters that Memorize Training Examples

Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller

MDPose: real-time multi-person pose estimation via mixture density model

Seunghyeon Seo, Jaeyoung Yoo, Jihye Hwang, Nojun Kwak

Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective

Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Gauillaud

Lifelong bandit optimization: no prior and no regret

Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause

Local Message Passing on Frustrated Systems

Luca Schmid, Joshua Brenk, Laurent Schmalen

Learning good interventions in causal graphs via covering

Ayush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman

Online Heavy-tailed Change-point detection

Abishek Sankararaman, Balakrishnan Narayanaswamy

Heteroskedastic Geospatial Tracking with Distributed Camera Networks

Colin Samplawski, Shiwei Fang, Ziqi Wang, Deepak Ganesan, Mani Srivastava

Is the volume of a credal set a good measure for epistemic uncertainty?

Yusuf Sale, Michele Caprio, Eyke Hüllermeier

Semi-supervised learning of partial differential operators and dynamical flows

Michael Rotman, Amit Dekel, Ran Ilan Ber, Lior Wolf, Yaron Oz

Hallucinated adversarial control for conservative offline policy evaluation

Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause

Approximately Bayes-optimal pseudo-label selection

Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin

The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process models

Steffen Ridderbusch, Sina Ober-Blöbaum, Paul Goulart

Inference for probabilistic dependency graphs

Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa

Validation of composite systems by discrepancy propagation

David Reeb, Kanil Patel, Karim Said Barsim, Martin Schiegg, Sebastian Gerwinn

Contrastive learning for supervised graph matching

Gathika Ratnayaka, Qing Wang, Yang Li

USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution

Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee

Jana: Jointly amortized neural approximation of complex Bayesian models

Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe

Split, count, and share: a differentially private set intersection cardinality estimation protocol

Michael Purcell, Yang Li, Kee Siong Ng

Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for Classification

Paweł Piwek, Adam Klukowski, Tianyang Hu

Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFO

Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter

Copula for Instance-wise Feature Selection and Rank

Hanyu Peng, Guanhua Fang, Ping Li

Multi-View Independent Component Analysis with Shared and Individual Sources

Teodora Pandeva, Patrick Forré

Stochastic Generative Flow Networks

Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio

Maximizing submodular functions under submodular constraints

Madhavan R. Padmanabhan, Yanhui Zhu, Samik Basu, A. Pavan

Bayesian numerical integration with neural networks

Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol

Structure-aware robustness certificates for graph classification

Pierre Osselin, Henry Kenlay, Xiaowen Dong

Approximate Thompson Sampling via Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi

Graph classification Gaussian processes via spectral features

Felix L. Opolka, Yin-Cong Zhi, Pietro Lió, Xiaowen Dong

Incentivizing honest performative predictions with proper scoring rules

Caspar Oesterheld, Johannes Treutlein, Emery Cooper, Rubi Hudson

An improved variational approximate posterior for the deep Wishart process

Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison

Size-constrained k-submodular maximization in near-linear time

Guanyu Nie, Yanhui Zhu, Yididiya Y. Nadew, Samik Basu, A. Pavan

Efficient Failure Pattern Identification of Predictive Algorithms

Bao Nguyen, Viet Anh Nguyen