UAI 2023

243 papers

$e(2)$-Equivariant Vision Transformer Renjun Xu, Kaifan Yang, Ke Liu, Fengxiang He

A Bayesian Approach for Bandit Online Optimization with Switching Cost Zai Shi, Jian Tan, Feifei Li

A Constrained Bayesian Approach to Out-of-Distribution Prediction Ziyu Wang, Binjie Yuan, Jiaxun Lu, Bowen Ding, Yunfeng Shao, Qibin Wu, Jun Zhu

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A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models Sinong Geng, Houssam Nassif, Carlos A. Manzanares

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A Decoder Suffices for Query-Adaptive Variational Inference Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth

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A Near-Optimal High-Probability Swap-Regret Upper Bound for Multi-Agent Bandits in Unknown General-Sum Games Zhiming Huang, Jianping Pan

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A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi

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A Policy Gradient Approach for Optimization of Smooth Risk Measures Nithia Vijayan, L. A. Prashanth

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A Scalable Walsh-Hadamard Regularizer to Overcome the Low-Degree Spectral Bias of Neural Networks Ali Gorji, Andisheh Amrollahi, Andreas Krause

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A Trajectory Is Worth Three Sentences: Multimodal Transformer for Offline Reinforcement Learning Yiqi Wang, Mengdi Xu, Laixi Shi, Yuejie Chi

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Accelerating Voting by Quantum Computation Ao Liu, Qishen Han, Lirong Xia, Nengkun Yu

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Active Metric Learning and Classification Using Similarity Queries Namrata Nadagouda, Austin Xu, Mark A. Davenport

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Adaptive Conditional Quantile Neural Processes Peiman Mohseni, Nick Duffield, Bani Mallick, Arman Hasanzadeh

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Adaptivity Complexity for Causal Graph Discovery Davin Choo, Kirankumar Shiragur

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Aligned Diffusion Schrödinger Bridges Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, Maria Rodriguez Martinez, Andreas Krause, Charlotte Bunne

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Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels Matthias Bitzer, Mona Meister, Christoph Zimmer

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An Effective Negotiating Agent Framework Based on Deep Offline Reinforcement Learning Siqi Chen, Jianing Zhao, Gerhard Weiss, Ran Su, Kaiyou Lei

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An Improved Variational Approximate Posterior for the Deep Wishart Process Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison

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Approximate Thompson Sampling via Epistemic Neural Networks Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

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Approximately Bayes-Optimal Pseudo-Label Selection Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin

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Approximating Probabilistic Explanations via Supermodular Minimization Louenas Bounia, Frederic Koriche

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Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-in-Time Adaptive Interventions Karine Karine, Predrag Klasnja, Susan A. Murphy, Benjamin M. Marlin

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ASTRA: Understanding the Practical Impact of Robustness for Probabilistic Programs Zixin Huang, Saikat Dutta, Sasa Misailovic

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AUC Maximization in Imbalanced Lifelong Learning Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu

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Bandits with Costly Reward Observations Aaron D. Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims

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Bayesian Inference Approach for Entropy Regularized Reinforcement Learning with Stochastic Dynamics Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin, Rahul V. Kulkarni

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Bayesian Inference for Vertex-Series-Parallel Partial Orders Chuxuan Jiang, Geoff K. Nicholls, Jeong Eun Lee

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Bayesian Numerical Integration with Neural Networks Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol

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BeliefPPG: Uncertainty-Aware Heart Rate Estimation from PPG Signals via Belief Propagation Valentin Bieri, Paul Streli, Berken Utku Demirel, Christian Holz

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Benefits of Monotonicity in Safe Exploration with Gaussian Processes Arpan Losalka, Jonathan Scarlett

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Benign Overfitting in Adversarially Robust Linear Classification Jinghui Chen, Yuan Cao, Quanquan Gu

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Best Arm Identification in Rare Events Anirban Bhattacharjee, Sushant Vijayan, Sandeep Juneja

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Bidirectional Attention as a Mixture of Continuous Word Experts Kevin C. Wibisono, Yixin Wang

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Birds of an Odd Feather: Guaranteed Out-of-Distribution (OOD) Novel Category Detection Yoav Wald, Suchi Saria

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BISCUIT: Causal Representation Learning from Binary Interactions Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves

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Blackbox Optimization of Unimodal Functions A. Cutkosky, A. Das, W. Kong, C. Lee, R. Sen

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Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter

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Bounding the Optimal Value Function in Compositional Reinforcement Learning Jacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas, Stas Tiomkin, Rahul V. Kulkarni

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Causal Discovery for Time Series from Multiple Datasets with Latent Contexts Wiebke Günther, Urmi Ninad, Jakob Runge

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Causal Discovery with Hidden Confounders Using the Algorithmic Markov Condition David Kaltenpoth, Jilles Vreeken

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Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators Klaus-Rudolf Kladny, Julius Kügelgen, Bernhard Schölkopf, Michael Muehlebach

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Causal Inference with Outcome-Dependent Missingness and Self-Censoring Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya

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Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing, Michaela Hardt

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Combinatorial Categorized Bandits with Expert Rankings Sayak Ray Chowdhury, Gaurav Sinha, Nagarajan Natarajan, Amit Sharma

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Composing Efficient, Robust Tests for Policy Selection Dustin Morrill, Thomas J. Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone

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Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata

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Conditional Abstraction Trees for Sample-Efficient Reinforcement Learning Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava

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Conditional Counterfactual Causal Effect for Individual Attribution Ruiqi Zhao, Lei Zhang, Shengyu Zhu, Zitong Lu, Zhenhua Dong, Chaoliang Zhang, Jun Xu, Zhi Geng, Yangbo He

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Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran

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Conformal Risk Control for Ordinal Classification Yunpeng Xu, Wenge Guo, Zhi Wei

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Content Sharing Design for Social Welfare in Networked Disclosure Game Feiran Jia, Chenxi Qiu, Sarah Rajtmajer, Anna Squicciarini

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Contrastive Learning for Supervised Graph Matching Gathika Ratnayaka, Qing Wang, Yang Li

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Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman

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Copula for Instance-Wise Feature Selection and Rank Hanyu Peng, Guanhua Fang, Ping Li

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Copula-Based Deep Survival Models for Dependent Censoring Ali Hossein Foomani Gharari, Michael Cooper, Russell Greiner, Rahul G Krishnan

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Correcting for Selection Bias and Missing Response in Regression Using Privileged Information P Boeken, Noud Kroon, Mathijs Jong, Joris M. Mooij, Onno Zoeter

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Counting Background Knowledge Consistent Markov Equivalent Directed Acyclic Graphs Vidya Sagar Sharma

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CrysMMNet: Multimodal Representation for Crystal Property Prediction Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly

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CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models Jiazheng Li, Zhaoyue Sun, Bin Liang, Lin Gui, Yulan He

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Deep Gaussian Mixture Ensembles Yousef El-Laham, Niccolo Dalmasso, Elizabeth Fons, Svitlana Vyetrenko

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DeepGD3: Unknown-Aware Deep Generative/Discriminative Hybrid Defect Detector for PCB Soldering Inspection Ching-Wen Ma, Yanwei Liu

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Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam

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Differentiable User Models Alex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski

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Differential Privacy in Cooperative Multiagent Planning Bo Chen, Calvin Hawkins, Mustafa O. Karabag, Cyrus Neary, Matthew Hale, Ufuk Topcu

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Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited Jinyan Su, Changhong Zhao, Di Wang

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Differentially Private Synthetic Data Using KD-Trees Eleonora Kreačić, Navid Nouri, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

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Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting A. Das, W. Kong, B. Paria, R. Sen

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Diversity-Enhanced Probabilistic Ensemble for Uncertainty Estimation Hanjing Wang, Qiang Ji

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Do We Become Wiser with Time? on Causal Equivalence with Tiered Background Knowledge Christine W. Bang, Vanessa Didelez

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Does Momentum Help in Stochastic Optimization? a Sample Complexity Analysis. Swetha Ganesh, Rohan Deb, Gugan Thoppe, Amarjit Budhiraja

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Efficient Failure Pattern Identification of Predictive Algorithms Bao Nguyen, Viet Anh Nguyen

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Efficient Learning of Minimax Risk Classifiers in High Dimensions Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez

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Efficient Privacy-Preserving Stochastic Nonconvex Optimization Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu

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Efficiently Learning the Graph for Semi-Supervised Learning Dravyansh Sharma, Maxwell Jones

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Energy-Based Predictive Representations for Partially Observed Reinforcement Learning Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai

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Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls Using the Double Penalty Integration Estimator Yuwen Cheng, Lili Wu, Shu Yang

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Establishing Markov Equivalence in Cyclic Directed Graphs Tom Claassen, Joris M. Mooij

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Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for Classification Paweł Piwek, Adam Klukowski, Tianyang Hu

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Expectation Consistency for Calibration of Neural Networks Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

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Exploiting Inferential Structure in Neural Processes Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric Nalisnick

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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, Don Towsley, John C.S. Lui

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Fairness-Aware Class Imbalanced Learning on Multiple Subgroups Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, Li Shen

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Fast and Scalable Score-Based Kernel Calibration Tests Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton

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Fast Heterogeneous Federated Learning with Hybrid Client Selection Duanxiao Song, Guangyuan Shen, Dehong Gao, Libin Yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou

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Fast Teammate Adaptation in the Presence of Sudden Policy Change Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue, Chengxing Jia, Cong Guan, Chao Qian, Yang Yu

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Fed-LAMB: Layer-Wise and Dimension-Wise Locally Adaptive Federated Learning Belhal Karimi, Ping Li, Xiaoyun Li

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Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs Tengfei Ma, Trong Nghia Hoang, Jie Chen

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Finding Invariant Predictors Efficiently via Causal Structure Kenneth Lee, Md Musfiqur Rahman, Murat Kocaoglu

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Finite-Sample Guarantees for Nash Q-Learning with Linear Function Approximation Pedro Cisneros-Velarde, Sanmi Koyejo

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Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances Anusha Lalitha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton

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FLASH: Automating Federated Learning Using CASH Md I. I. Alam, Koushik Kar, Theodoros Salonidis, Horst Samulowitz

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Functional Causal Bayesian Optimization Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa

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Gaussian Process Surrogate Models for Neural Networks Michael Y. Li, Erin Grant, Thomas L. Griffiths

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Generating Synthetic Datasets by Interpolating Along Generalized Geodesics Jiaojiao Fan, David Alvarez-Melis

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Graph Classification Gaussian Processes via Spectral Features Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong

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Graph Self-Supervised Learning via Proximity Distribution Minimization Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava

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Greed Is Good: Correspondence Recovery for Unlabeled Linear Regression Hang Zhang, Ping Li

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Guided Deep Kernel Learning Idan Achituve, Gal Chechik, Ethan Fetaya

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Hallucinated Adversarial Control for Conservative Offline Policy Evaluation Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause

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Heavy-Tailed Linear Bandit with Huber Regression Minhyun Kang, Gi-Soo Kim

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Heteroskedastic Geospatial Tracking with Distributed Camera Networks Colin Samplawski, Shiwei Fang, Ziqi Wang, Deepak Ganesan, Mani Srivastava, Benjamin M. Marlin

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How to Use Dropout Correctly on Residual Networks with Batch Normalization Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Donggeon Lee, Sang Woo Kim

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Human Control: Definitions and Algorithms Ryan Carey, Tom Everitt

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Human-in-the-Loop Mixup Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, Adrian Weller

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Implicit Training of Inference Network Models for Structured Prediction Shiv Shankar

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Improvable Gap Balancing for Multi-Task Learning Yanqi Dai, Nanyi Fei, Zhiwu Lu

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In- or Out-of-Distribution Detection via Dual Divergence Estimation Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka

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Incentivising Diffusion While Preserving Differential Privacy Fengjuan. Jia, Mengxiao. Zhang, Jiamou. Liu, Bakh Khoussainov

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Incentivizing Honest Performative Predictions with Proper Scoring Rules Caspar Oesterheld, Johannes Treutlein, Emery Cooper, Rubi Hudson

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Increasing Effect Sizes of Pairwise Conditional Independence Tests Between Random Vectors Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge

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Inference and Sampling of Point Processes from Diffusion Excursions Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh

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Inference for Mark-Censored Temporal Point Processes Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth

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Inference for Probabilistic Dependency Graphs Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa

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Inference of a Rumor’s Source in the Independent Cascade Model Petra Berenbrink, Max Hahn-Klimroth, Dominik Kaaser, Lena Krieg, Malin Rau

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Information Theoretic Clustering via Divergence Maximization Among Clusters Sahil Garg, Mina Dalirrooyfard, Anderson Schneider, Yeshaya Adler, Yuriy Nevmyvaka, Yu Chen, Fengpei Li, Guillermo Cecchi

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Interpretable Differencing of Machine Learning Models Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly

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Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals Michael Jahn, Matthias Scheutz

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Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? Yusuf Sale, Michele Caprio, Eyke Hüllermeier

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Jana: Jointly Amortized Neural Approximation of Complex Bayesian Models Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner

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Keep-Alive Caching for the Hawkes Process Sushirdeep Narayana, Ian A. Kash

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Knowledge Intensive Learning of Cutset Networks Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan

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KrADagrad: Kronecker Approximation-Domination Gradient Preconditioned Stochastic Optimization Jonathan Mei, Alexander Moreno, Luke Walters

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Learning Choice Functions with Gaussian Processes Alessio Benavoli, Dario Azzimonti, Dario Piga

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Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Gauillaud

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Learning Good Interventions in Causal Graphs via Covering Ayush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman

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Learning in Online MDPs: Is There a Price for Handling the Communicating Case? Gautam Chandrasekaran, Ambuj Tewari

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Learning Nonlinear Causal Effect via Kernel Anchor Regression Wenqi Shi, Wenkai Xu

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Learning Robust Representation for Reinforcement Learning with Distractions by Reward Sequence Prediction Qi Zhou, Jie Wang, Qiyuan Liu, Yufei Kuang, Wengang Zhou, Houqiang Li

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Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger

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Learning to Reason About Contextual Knowledge for Planning Under Uncertainty Cheng Cui, Saeid Amiri, Yan Ding, Xingyue Zhan, Shiqi Zhang

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Lifelong Bandit Optimization: No Prior and No Regret Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause

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Local Message Passing on Frustrated Systems Luca Schmid, Joshua Brenk, Laurent Schmalen

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Locally Regularized Sparse Graph by Fast Proximal Gradient Descent Dongfang Sun, Yingzhen Yang

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Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties Yassir Fathullah, Guoxuan Xia, Mark J. F. Gales

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Loosely Consistent Emphatic Temporal-Difference Learning Jiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood

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Low-Rank Matrix Recovery with Unknown Correspondence Zhiwei Tang, Tsung-Hui Chang, Xiaojing Ye, Hongyuan Zha

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Massively Parallel Reweighted Wake-Sleep Thomas Heap, Gavin Leech, Laurence Aitchison

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Maximizing Submodular Functions Under Submodular Constraints Madhavan R. Padmanabhan, Yanhui Zhu, Samik Basu, A. Pavan

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MDPose: Real-Time Multi-Person Pose Estimation via Mixture Density Model Seunghyeon Seo, Jaeyoung Yoo, Jihye Hwang, Nojun Kwak

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Memory Mechanism for Unsupervised Anomaly Detection Jiahao Li, Yiqiang Chen, Yunbing Xing

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Meta-Learning Control Variates: Variance Reduction with Limited Data Zhuo Sun, Chris J Oates, François-Xavier Briol

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MFA: Multi-Layer Feature-Aware Attack for Object Detection Wen Chen, Yushan Zhang, Zhiheng Li, Yuehuan Wang

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Mitigating Transformer Overconfidence via Lipschitz Regularization Wenqian Ye, Yunsheng Ma, Xu Cao, Kun Tang

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Mixture of Normalizing Flows for European Option Pricing Yongxin Yang, Timothy M. Hospedales

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MixupE: Understanding and Improving Mixup from Directional Derivative Perspective Yingtian Zou, Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi

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MMEL: A Joint Learning Framework for Multi-Mention Entity Linking Chengmei Yang, Bowei He, Yimeng Wu, Chao Xing, Lianghua He, Chen Ma

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Mnemonist: Locating Model Parameters That Memorize Training Examples Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller

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Modified Retrace for Off-Policy Temporal Difference Learning Xingguo Chen, Xingzhou Ma, Yang Li, Guang Yang, Shangdong Yang, Yang Gao

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Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu

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Monte-Carlo Search for an Equilibrium in Dec-POMDPs Yang You, Vincent Thomas, Francis Colas, Olivier Buffet

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Multi-Modal Differentiable Unsupervised Feature Selection Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe

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Multi-View Graph Contrastive Learning for Solving Vehicle Routing Problems Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang

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Multi-View Independent Component Analysis with Shared and Individual Sources Teodora Pandeva, Patrick Forré

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Neural Probabilistic Logic Programming in Discrete-Continuous Domains Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt

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Neural Tangent Kernel at Initialization: Linear Width Suffices Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin

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No-Regret Linear Bandits Beyond Realizability Chong Liu, Ming Yin, Yu-Xiang Wang

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Noisy Adversarial Representation Learning for Effective and Efficient Image Obfuscation Jonghu Jeong, Minyong Cho, Philipp Benz, Tae-hoon Kim

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Nonconvex Stochastic Scaled Gradient Descent and Generalized Eigenvector Problems Chris Junchi Li, Michael I Jordan

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Nyström $m$-Hilbert-Schmidt Independence Criterion Florian Kalinke, Zoltán Szabó

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On Identifiability of Conditional Causal Effects Yaroslav Kivva, Jalal Etesami, Negar Kiyavash

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On Inference and Learning with Probabilistic Generating Circuits Juha Harviainen, Vaidyanathan Peruvemba Ramaswamy, Mikko Koivisto

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On Minimizing the Impact of Dataset Shifts on Actionable Explanations Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju

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On Testability and Goodness of Fit Tests in Missing Data Models Razieh Nabi, Rohit Bhattacharya

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On the Convergence of Continual Learning with Adaptive Methods Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee

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On the Informativeness of Supervision Signals Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths

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On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks Joar Skalse, Alessandro Abate

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On the Relation Between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation Alberto Maria Metelli, Samuele Meta, Marcello Restelli

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On the Role of Generalization in Transferability of Adversarial Examples Yilin Wang, Farzan Farnia

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On the Role of Model Uncertainties in Bayesian Optimisation Jonathan Foldager, Mikkel Jordahn, Lars K. Hansen, Michael R. Andersen

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Online Estimation of Similarity Matrices with Incomplete Data Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li

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Online Heavy-Tailed Change-Point Detection Abishek Sankararaman, Balakrishnan Narayanaswamy

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Optimal Budget Allocation for Crowdsourcing Labels for Graphs Adithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li

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Optimistic Thompson Sampling-Based Algorithms for Episodic Reinforcement Learning Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt

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Overcoming Language Priors for Visual Question Answering via Loss Rebalancing Label and Global Context Runlin Cao, Zhixin Li

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Pandering in a (flexible) Representative Democracy Xiaolin Sun, Jacob Masur, Ben Abramowitz, Nicholas Mattei, Zizhan Zheng

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Parity Calibration Youngseog Chung, Aaron Rumack, Chirag Gupta

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Partial Identification of Dose Responses with Hidden Confounders Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg

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Personalized Federated Domain Adaptation for Item-to-Item Recommendation Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang

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Pessimistic Model Selection for Offline Deep Reinforcement Learning Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, Pin-Yu Chen

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Phase-Shifted Adversarial Training Yeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin

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Piecewise Deterministic Markov Processes for Bayesian Neural Networks Ethan Goan, Dimitri Perrin, Kerrie Mengersen, Clinton Fookes

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Posterior Sampling-Based Online Learning for the Stochastic Shortest Path Model Mehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain, Haipeng Luo

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Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, Zenglin Xu

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Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang

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Probabilistic Circuits That Know What They Don’t Know Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting

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Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan

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Probabilistic Multi-Dimensional Classification Vu-Linh Nguyen, Yang Yang, Cassio De Campos

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Probabilistically Robust Conformal Prediction Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja, Yan Yan, Jana Doppa, Brian Jones

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Provably Efficient Adversarial Imitation Learning with Unknown Transitions Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo

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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

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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

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Quantifying Lottery Tickets Under Label Noise: Accuracy, Calibration, and Complexity Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti

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Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates Sahra Ghalebikesabi, Chris C. Holmes, Edwin Fong, Brieuc Lehmann

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Random Reshuffling with Variance Reduction: New Analysis and Better Rates Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik

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RDM-DC: Poisoning Resilient Dataset Condensation with Robust Distribution Matching Tianhang Zheng, Baochun Li

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Regularized Online DR-Submodular Optimization Pengyu Zuo, Yao Wang, Shaojie Tang

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Residual-Based Error Bound for Physics-Informed Neural Networks Shuheng Liu, Xiyue Huang, Pavlos Protopapas

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Revisiting Bayesian Network Learning with Small Vertex Cover Juha Harviainen, Mikko Koivisto

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Reward-Machine-Guided, Self-Paced Reinforcement Learning Cevahir Koprulu, Ufuk Topcu

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Risk-Aware Curriculum Generation for Heavy-Tailed Task Distributions Cevahir Koprulu, Thiago D. Simão, Nils Jansen, Ufuk Topcu

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Risk-Limiting Financial Audits via Weighted Sampling Without Replacement Shubhanshu Shekhar, Ziyu Xu, Zachary Lipton, Pierre Liang, Aaditya Ramdas

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Robust Distillation for Worst-Class Performance: On the Interplay Between Teacher and Student Objectives Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon

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Robust Gaussian Process Regression with the Trimmed Marginal Likelihood Daniel Andrade, Akiko Takeda

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Robust Quickest Change Detection for Unnormalized Models Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh

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Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin

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Sample Boosting Algorithm (SamBA) - An Interpretable Greedy Ensemble Classifier Based on Local Expertise for Fat Data Baptiste Bauvin, Cécile Capponi, Florence Clerc, Pascal Germain, Sokol Koço, Jacques Corbeil

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Scalable and Robust Tensor Ring Decomposition for Large-Scale Data Yicong He, George K. Atia

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Scalable Nonparametric Bayesian Learning for Dynamic Velocity Fields Sunrit Chakraborty, Aritra Guha, Rayleigh Lei, XuanLong Nguyen

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Scaling Integer Arithmetic in Probabilistic Programs William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd Millstein, Guy Van den Broeck

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Semi-Supervised Learning of Partial Differential Operators and Dynamical Flows Michael Rotman, Amit Dekel, Ran Ilan Ber, Lior Wolf, Yaron Oz

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Simple Transferability Estimation for Regression Tasks Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho, Vu Dinh, Anh T. Tran, Tal Hassner, Cuong V. Nguyen

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Size-Constrained K-Submodular Maximization in Near-Linear Time Guanyu Nie, Yanhui Zhu, Yididiya Y. Nadew, Samik Basu, A. Pavan, Christopher John Quinn

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Solving Multi-Model MDPs by Coordinate Ascent and Dynamic Programming Xihong Su, Marek Petrik

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SPDF: Sparse Pre-Training and Dense Fine-Tuning for Large Language Models Vithursan Thangarasa, Abhay Gupta, William Marshall, Tianda Li, Kevin Leong, Dennis DeCoste, Sean Lie, Shreyas Saxena

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Split, Count, and Share: A Differentially Private Set Intersection Cardinality Estimation Protocol Michael Purcell, Yang Li, Kee Siong Ng

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Stochastic Generative Flow Networks Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio

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Stochastic Graphical Bandits with Heavy-Tailed Rewards Yutian Gou, Jinfeng Yi, Lijun Zhang

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Structure-Aware Robustness Certificates for Graph Classification Pierre Osselin, Henry Kenlay, Xiaowen Dong

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Studying the Effect of GNN Spatial Convolutions on the Embedding Space’s Geometry Claire Donnat, So Won Jeong

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SubMix: Learning to Mix Graph Sampling Heuristics Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, Mohammadhossein Bateni

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Sufficient Identification Conditions and Semiparametric Estimation Under Missing Not at Random Mechanisms Anna Guo, Jiwei Zhao, Razieh Nabi

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SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs Vishal Sharma, Daman Arora, Mausam, Parag Singla

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TCE: A Test-Based Approach to Measuring Calibration Error Takuo Matsubara, Niek Tax, Richard Mudd, Ido Guy

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Testing Conventional Wisdom (of the Crowd) Noah Burrell, Grant Schoenebeck

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The past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models Steffen Ridderbusch, Sina Ober-Blöbaum, Paul Goulart

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The Shrinkage-Delinkage Trade-Off: An Analysis of Factorized Gaussian Approximations for Variational Inference Charles C. Margossian, Lawrence K. Saul

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Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction Chengmin Gao, Bin Li

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Towards Better Certified Segmentation via Diffusion Models Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Marie-Pierre Revel, Siddharth Garg, Farshad Khorrami, Maria Vakalopoulou

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Towards Physically Reliable Molecular Representation Learning Seunghoon Yi, Youngwoo Cho, Jinhwan Sul, Seung Woo Ko, Soo Kyung Kim, Jaegul Choo, Hongkee Yoon, Joonseok Lee

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Transfer Learning for Individual Treatment Effect Estimation Ahmed Aloui, Juncheng Dong, Cat P Le, Vahid Tarokh

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Two Sides of Miscalibration: Identifying over and Under-Confidence Prediction for Network Calibration Shuang Ao, Stefan Rueger, Advaith Siddharthan

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Two-Phase Attacks in Security Games Andrzej Nagorko, Pawel Ciosmak, Tomasz Michalak

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Two-Stage Holistic and Contrastive Explanation of Image Classification Weiyan Xie, Xiao-Hui Li, Zhi Lin, Leonard K. M. Poon, Caleb Chen Cao, Nevin L. Zhang

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Two-Stage Kernel Bayesian Optimization in High Dimensions Jian Tan, Niv Nayman

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Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension Yue Wu, Jiafan He, Quanquan Gu

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Universal Graph Contrastive Learning with a Novel Laplacian Perturbation Taewook Ko, Yoonhyuk Choi, Chong-Kwon Kim

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USIM-DAL: Uncertainty-Aware Statistical Image Modeling-Based Dense Active Learning for Super-Resolution Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee

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Vacant Holes for Unsupervised Detection of the Outliers in Compact Latent Representation Misha Glazunov, Apostolis Zarras

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Validation of Composite Systems by Discrepancy Propagation David Reeb, Kanil Patel, Karim Said Barsim, Martin Schiegg, Sebastian Gerwinn

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Variable Importance Matching for Causal Inference Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page

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ViBid: Linear Vision Transformer with Bidirectional Normalization Jeonggeun Song, Heung-Chang Lee

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When Are Post-Hoc Conceptual Explanations Identifiable? Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci

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Why Out-of-Distribution Detection Experiments Are Not Reliable - Subtle Experimental Details Muddle the OOD Detector Rankings Kamil Szyc, Tomasz Walkowiak, Henryk Maciejewski

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