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Volume 216: Uncertainty in Artificial Intelligence, 31-4 August 2023, Pittsburgh, PA, USA

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Editors: Robin J. Evans, Ilya Shpitser

[bib][citeproc]

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1-10

Guided Deep Kernel Learning

Idan Achituve, Gal Chechik, Ethan Fetaya; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:11-21

Bounding the optimal value function in compositional reinforcement learning

Jacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas, Stas Tiomkin, Rahul V. Kulkarni; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:22-32

A decoder suffices for query-adaptive variational inference

Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:33-44

FLASH: Automating federated learning using CASH

Md I. I. Alam, Koushik Kar, Theodoros Salonidis, Horst Samulowitz; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:45-55

Transfer learning for individual treatment effect estimation

Ahmed Aloui, Juncheng Dong, Cat P Le, Vahid Tarokh; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:56-66

Robust Gaussian process regression with the trimmed marginal likelihood

Daniel Andrade, Akiko Takeda; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:67-76

Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration

Shuang Ao, Stefan Rueger, Advaith Siddharthan; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:77-87

Quantifying lottery tickets under label noise: accuracy, calibration, and complexity

Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:88-98

Bayesian inference approach for entropy regularized reinforcement learning with stochastic dynamics

Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin, Rahul V. Kulkarni; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:99-109

Neural tangent kernel at initialization: linear width suffices

Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:110-118

Do we become wiser with time? On causal equivalence with tiered background knowledge

Christine W. Bang, Vanessa Didelez; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:119-129

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:130-140

Learning Choice Functions with Gaussian Processes

Alessio Benavoli, Dario Azzimonti, Dario Piga; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:141-151

Inference of a rumor’s source in the independent cascade model

Petra Berenbrink, Max Hahn-Klimroth, Dominik Kaaser, Lena Krieg, Malin Rau; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:152-162

Best arm identification in rare events

Anirban Bhattacharjee, Sushant Vijayan, Sandeep Juneja; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:163-172

BeliefPPG: Uncertainty-aware heart rate estimation from PPG signals via belief propagation

Valentin Bieri, Paul Streli, Berken Utku Demirel, Christian Holz; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:173-183

Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels

Matthias Bitzer, Mona Meister, Christoph Zimmer; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:184-194

Correcting for selection bias and missing response in regression using privileged information

P Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:195-205

Efficient Learning of Minimax Risk Classifiers in High Dimensions

Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:206-215

Approximating probabilistic explanations via supermodular minimization

Louenas Bounia, Frederic Koriche; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:216-225

Inference for mark-censored temporal point processes

Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:226-236

Testing conventional wisdom (of the crowd)

Noah Burrell, Grant Schoenebeck; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:237-248

Overcoming Language Priors for Visual Question Answering via Loss Rebalancing Label and Global Context

Runlin Cao, Zhixin Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:249-259

Scaling integer arithmetic in probabilistic programs

William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd Millstein, Guy Van den Broeck; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:260-270

Human Control: Definitions and Algorithms

Ryan Carey, Tom Everitt; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:271-281

Scalable nonparametric Bayesian learning for dynamic velocity fields

Sunrit Chakraborty, Aritra Guha, Rayleigh Lei, XuanLong Nguyen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:282-292

Learning in online MDPs: is there a price for handling the communicating case?

Gautam Chandrasekaran, Ambuj Tewari; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:293-302

Modified Retrace for Off-Policy Temporal Difference Learning

Xingguo Chen, Xingzhou Ma, Yang Li, Guang Yang, Shangdong Yang, Yang Gao; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:303-312

Benign Overfitting in Adversarially Robust Linear Classification

Jinghui Chen, Yuan Cao, Quanquan Gu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:313-323

An effective negotiating agent framework based on deep offline reinforcement learning

Siqi Chen, Jianing Zhao, Gerhard Weiss, Ran Su, Kaiyou Lei; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:324-335

MFA: Multi-layer Feature-aware Attack for Object Detection

Wen Chen, Yushan Zhang, Zhiheng Li, Yuehuan Wang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:336-346

Differential Privacy in Cooperative Multiagent Planning

Bo Chen, Calvin Hawkins, Mustafa O. Karabag, Cyrus Neary, Matthew Hale, Ufuk Topcu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:347-357

Causal inference with outcome-dependent missingness and self-censoring

Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:358-368

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:369-380

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:381-390

Adaptivity Complexity for Causal Graph Discovery

Davin Choo, Kirankumar Shiragur; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:391-402

Combinatorial categorized bandits with expert rankings

Sayak Ray Chowdhury, Gaurav Sinha, Nagarajan Natarajan, Amit Sharma; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:403-412

Parity calibration

Youngseog Chung, Aaron Rumack, Chirag Gupta; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:413-423

Finite-sample guarantees for Nash Q-learning with linear function approximation

Pedro Cisneros-Velarde, Sanmi Koyejo; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:424-432

Establishing Markov equivalence in cyclic directed graphs

Tom Claassen, Joris M. Mooij; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:433-442

Expectation consistency for calibration of neural networks

Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:443-453

Human-in-the-Loop Mixup

Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, Adrian Weller; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:454-464

Learning to reason about contextual knowledge for planning under uncertainty

Cheng Cui, Saeid Amiri, Yan Ding, Xingyue Zhan, Shiqi Zhang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:465-475

Blackbox optimization of unimodal functions

A. Cutkosky, A. Das, W. Kong, C. Lee, R. Sen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:476-484

Conditional abstraction trees for sample-efficient reinforcement learning

Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:485-495

Improvable Gap Balancing for Multi-Task Learning

Yanqi Dai, Nanyi Fei, Zhiwu Lu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:496-506

CrysMMNet: Multimodal Representation for Crystal Property Prediction

Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:507-517

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting

A. Das, W. Kong, B. Paria, R. Sen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:518-528

Neural probabilistic logic programming in discrete-continuous domains

Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Readt; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:529-538

Studying the Effect of GNN Spatial Convolutions On The Embedding Space’s Geometry

Claire Donnat, So Won Jeong; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:539-548

Deep Gaussian mixture ensembles

Yousef El-Laham, Niccolo Dalmasso, Elizabeth Fons, Svitlana Vyetrenko; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:549-559

Personalized federated domain adaptation for item-to-item recommendation

Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:560-570

Generating Synthetic Datasets by Interpolating along Generalized Geodesics

Jiaojiao Fan, David Alvarez-Melis; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:571-581

Logit-based ensemble distribution distillation for robust autoregressive sequence uncertainties

Yassir Fathullah, Guoxuan Xia, Mark J. F. Gales; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:582-591

On the role of model uncertainties in Bayesian optimisation

Jonathan Foldager, Mikkel Jordahn, Lars K. Hansen, Michael R. Andersen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:592-601

Does Momentum Help in Stochastic Optimization? A Sample Complexity Analysis.

Swetha Ganesh, Rohan Deb, Gugan Thoppe, Amarjit Budhiraja; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:602-612

Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction

Chengmin Gao, Bin Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:613-623

Information theoretic clustering via divergence maximization among clusters

Sahil Garg, Mina Dalirrooyfard, Anderson Schneider, Yeshaya Adler, Yuriy Nevmyvaka, Yu Chen, Fengpei Li, Guillermo Cecchi; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:624-634

In- or out-of-distribution detection via dual divergence estimation

Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:635-646

A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models

Sinong Geng, Houssam Nassif, Carlos A. Manzanares; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:647-657

Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates

Sahra Ghalebikesabi, Chris C. Holmes, Edwin Fong, Brieuc Lehmann; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:658-668

Copula-based deep survival models for dependent censoring

Ali Hossein Foomani Gharari, Michael Cooper, Russell Greiner, Rahul G Krishnan; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:669-680

Probabilistically robust conformal prediction

Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja, Yan Yan, Jana Doppa, Brian Jones; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:681-690

Fast and scalable score-based kernel calibration tests

Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:691-700

Vacant holes for unsupervised detection of the outliers in compact latent representation

Misha Glazunov, Apostolis Zarras; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:701-711

Piecewise Deterministic Markov Processes for Bayesian Neural Networks

Ethan Goan, Dimitri Perrin, Kerrie Mengersen, Clinton Fookes; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:712-722

A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks

Ali Gorji, Andisheh Amrollahi, Andreas Krause; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:723-733

Stochastic Graphical Bandits with Heavy-Tailed Rewards

Yutian Gou, Jinfeng Yi, Lijun Zhang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:734-744

Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow

Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:745-755

Functional causal Bayesian optimization

Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:756-765

Causal Discovery for time series from multiple datasets with latent contexts

Wiebke Günther, Urmi Ninad, Jakob Runge; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:766-776

Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms

Anna Guo, Jiwei Zhao, Razieh Nabi; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:777-787

Interpretable differencing of machine learning models

Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:788-797

Differentiable user models

Alex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:798-808

On the Convergence of Continual Learning with Adaptive Methods

Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:809-818

Revisiting Bayesian network learning with small vertex cover

Juha Harviainen, Mikko Koivisto; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:819-828

On inference and learning with probabilistic generating circuits

Juha Harviainen, Vaidyanathan Peruvemba Ramaswamy, Mikko Koivisto; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:829-838

Inference and sampling of point processes from diffusion excursions

Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:839-848

Loosely consistent emphatic temporal-difference learning

Jiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:849-859

Scalable and robust tensor ring decomposition for large-scale data

Yicong He, George K. Atia; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:860-869

Massively parallel reweighted wake-sleep

Thomas Heap, Gavin Leech, Laurence Aitchison; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:870-878

Increasing effect sizes of pairwise conditional independence tests between random vectors

Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:879-889

Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning

Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:890-899

ASTRA: Understanding the practical impact of robustness for probabilistic programs

Zixin Huang, Saikat Dutta, Sasa Misailovic; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:900-910

A near-optimal high-probability swap-Regret upper bound for multi-agent bandits in unknown general-sum games

Zhiming Huang, Jianping Pan; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:911-921

Posterior sampling-based online learning for the stochastic shortest path model

Mehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain, Haipeng Luo; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:922-931

Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals

Michael Jahn, Matthias Scheutz; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:932-940

Robust statistical comparison of random variables with locally varying scale of measurement

Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:941-952

Noisy adversarial representation learning for effective and efficient image obfuscation

Jonghu Jeong, Minyong Cho, Philipp Benz, Tae-hoon Kim; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:953-962

Incentivising Diffusion while Preserving Differential Privacy

Fengjuan. Jia, Mengxiao. Zhang, Jiamou. Liu, Bakh Khoussainov; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:963-972

Content Sharing Design for Social Welfare in Networked Disclosure Game

Feiran Jia, Chenxi Qiu, Sarah Rajtmajer, Anna Squicciarini; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:973-983

Multi-view graph contrastive learning for solving vehicle routing problems

Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:984-994

Bayesian inference for vertex-series-parallel partial orders

Chuxuan Jiang, Geoff K. Nicholls, Jeong Eun Lee; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:995-1004

Nyström $M$-Hilbert-Schmidt independence criterion

Florian Kalinke, Zoltán Szabó; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1005-1015

Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition

David Kaltenpoth, Jilles Vreeken; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1016-1026

Heavy-tailed linear bandit with Huber regression

Minhyun Kang, Gi-Soo Kim; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1027-1036

Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning

Belhal Karimi, Ping Li, Xiaoyun Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1037-1046

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1047-1057

How to use dropout correctly on residual networks with batch normalization

Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Donggeon Lee, Sang Woo Kim; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1058-1067

Phase-shifted adversarial training

Yeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1068-1077

On Identifiability of Conditional Causal Effects

Yaroslav Kivva, Jalal Etesami, Negar Kiyavash; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1078-1086

Causal effect estimation from observational and interventional data through matrix weighted linear estimators

Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1087-1097

Universal Graph Contrastive Learning with a Novel Laplacian Perturbation

Taewook Ko, Yoonhyuk Choi, Chong-Kwon Kim; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1098-1108

Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting

Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1109-1120

Reward-machine-guided, self-paced reinforcement learning

Cevahir Koprulu, Ufuk Topcu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1121-1131

Risk-aware curriculum generation for heavy-tailed task distributions

Cevahir Koprulu, Thiago D. Simão, Nils Jansen, Ufuk Topcu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1132-1142

Differentially private synthetic data using KD-trees

Eleonora Kreačić, Navid Nouri, Vamsi K. Potluru, Tucker Balch, Manuela Veloso; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1143-1153

Optimal Budget Allocation for Crowdsourcing Labels for Graphs

Adithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1154-1163

Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances

Anusha Lalitha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1164-1173

Variable importance matching for causal inference

Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1174-1184

Towards better certified segmentation via diffusion models

Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Marie-Pierre Revel, Siddharth Garg, Farshad Khorrami, Maria Vakalopoulou; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1185-1195

Finding Invariant Predictors Efficiently via Causal Structure

Kenneth Lee, Md Musfiqur Rahman, Murat Kocaoglu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1196-1206

When are post-hoc conceptual explanations identifiable?

Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1207-1218

Memory Mechanism for Unsupervised Anomaly Detection

Jiahao Li, Yiqiang Chen, Yunbing Xing; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1219-1229

Nonconvex stochastic scaled gradient descent and generalized eigenvector problems

Chris Junchi Li, Michael I Jordan; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1230-1240

Gaussian Process Surrogate Models for Neural Networks

Michael Y. Li, Erin Grant, Thomas L. Griffiths; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1241-1252

CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models

Jiazheng Li, Zhaoyue Sun, Bin Liang, Lin Gui, Yulan He; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1253-1262

BISCUIT: Causal Representation Learning from Binary Interactions

Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Efstratios Gavves; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1263-1273

Accelerating Voting by Quantum Computation

Ao Liu, Qishen Han, Lirong Xia, Nengkun Yu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1274-1283

Residual-based error bound for physics-informed neural networks

Shuheng Liu, Xiyue Huang, Pavlos Protopapas; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1284-1293

No-Regret Linear Bandits beyond Realizability

Chong Liu, Ming Yin, Yu-Xiang Wang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1294-1303

Benefits of monotonicity in safe exploration with Gaussian processes

Arpan Losalka, Jonathan Scarlett; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1304-1314

Practical privacy-preserving Gaussian process regression via secret sharing

Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, Zenglin Xu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1315-1325

DeepGD3: Unknown-Aware Deep Generative/Discriminative Hybrid Defect Detector for PCB Soldering Inspection

Ching-Wen Ma, Yanwei Liu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1326-1335

Federated learning of models pre-trained on different features with consensus graphs

Tengfei Ma, Trong Nghia Hoang, Jie Chen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1336-1346

Random Reshuffling with Variance Reduction: New Analysis and Better Rates

Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1347-1357

The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference

Charles C. Margossian, Lawrence K. Saul; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1358-1367

Partial identification of dose responses with hidden confounders

Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1368-1379

Knowledge Intensive Learning of Cutset Networks

Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1380-1389

TCE: A Test-Based Approach to Measuring Calibration Error

Takuo Matsubara, Niek Tax, Richard Mudd, Ido Guy; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1390-1400

Causal information splitting: Engineering proxy features for robustness to distribution shifts

Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing, Michaela Hardt; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1401-1411

KrADagrad: Kronecker approximation-domination gradient preconditioned stochastic optimization

Jonathan Mei, Alexander Moreno, Luke Walters; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1412-1422

On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation

Alberto Maria Metelli, Samuele Meta, Marcello Restelli; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1423-1433

On Minimizing the Impact of Dataset Shifts on Actionable Explanations

Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1434-1444

Adaptive Conditional Quantile Neural Processes

Peiman Mohseni, Nick Duffield, Bani Mallick, Arman Hasanzadeh; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1445-1455

Composing Efficient, Robust Tests for Policy Selection

Dustin Morrill, Thomas J. Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1456-1466

On Testability and Goodness of Fit Tests in Missing Data Models

Razieh Nabi, Rohit Bhattacharya; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1467-1477

Active metric learning and classification using similarity queries

Namrata Nadagouda, Austin Xu, Mark A. Davenport; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1478-1488

Two-phase Attacks in Security Games

Andrzej Nagorko, Pawel Ciosmak, Tomasz Michalak; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1489-1498

Keep-Alive Caching for the Hawkes process

Sushirdeep Narayana, Ian A. Kash; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1499-1509

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1510-1521

Probabilistic Multi-Dimensional Classification

Vu-Linh Nguyen, Yang Yang, Cassio De Campos; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1522-1533

Efficient Failure Pattern Identification of Predictive Algorithms

Bao Nguyen, Viet Anh Nguyen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1534-1544

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

Guanyu Nie, Yanhui Zhu, Yididiya Y. Nadew, Samik Basu, A. Pavan, Christopher John Quinn; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1545-1554

An improved variational approximate posterior for the deep Wishart process

Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1555-1563

Incentivizing honest performative predictions with proper scoring rules

Caspar Oesterheld, Johannes Treutlein, Emery Cooper, Rubi Hudson; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1564-1574

Graph classification Gaussian processes via spectral features

Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1575-1585

Approximate Thompson Sampling via Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1586-1595

Structure-aware robustness certificates for graph classification

Pierre Osselin, Henry Kenlay, Xiaowen Dong; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1596-1605

Baysian numerical integration with neural networks

Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1606-1617

Maximizing submodular functions under submodular constraints

Madhavan R. Padmanabhan, Yanhui Zhu, Samik Basu, A. Pavan; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1618-1627

Stochastic Generative Flow Networks

Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1628-1638

Multi-View Independent Component Analysis with Shared and Individual Sources

Teodora Pandeva, Patrick Forré; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1639-1650

Copula for Instance-wise Feature Selection and Rank

Hanyu Peng, Guanhua Fang, Ping Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1651-1661

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

Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1662-1672

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

Paweł Piwek, Adam Klukowski, Tianyang Hu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1673-1683

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

Michael Purcell, Yang Li, Kee Siong Ng; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1684-1694

Jana: Jointly amortized neural approximation of complex Bayesian models

Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1695-1706

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

Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1707-1717

Contrastive learning for supervised graph matching

Gathika Ratnayaka, Qing Wang, Yang Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1718-1729

Validation of composite systems by discrepancy propagation

David Reeb, Kanil Patel, Karim Said Barsim, Martin Schiegg, Sebastian Gerwinn; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1730-1740

Inference for probabilistic dependency graphs

Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1741-1751

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

Steffen Ridderbusch, Sina Ober-Blöbaum, Paul Goulart; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1752-1761

Approximately Bayes-optimal pseudo-label selection

Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1762-1773

Hallucinated adversarial control for conservative offline policy evaluation

Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1774-1784

Semi-supervised learning of partial differential operators and dynamical flows

Michael Rotman, Amit Dekel, Ran Ilan Ber, Lior Wolf, Yaron Oz; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1785-1794

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

Yusuf Sale, Michele Caprio, Eyke Höllermeier; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1795-1804

Heteroskedastic Geospatial Tracking with Distributed Camera Networks

Colin Samplawski, Shiwei Fang, Ziqi Wang, Deepak Ganesan, Mani Srivastava, Benjamin M. Marlin; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1805-1814

Online Heavy-tailed Change-point detection

Abishek Sankararaman, Balakrishnan Narayanaswamy; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1815-1826

Learning good interventions in causal graphs via covering

Ayush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1827-1836

Local Message Passing on Frustrated Systems

Luca Schmid, Joshua Brenk, Laurent Schmalen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1837-1846

Lifelong bandit optimization: no prior and no regret

Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1847-1857

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

Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Gauillaud; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1858-1867

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

Seunghyeon Seo, Jaeyoung Yoo, Jihye Hwang, Nojun Kwak; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1868-1878

Mnemonist: Locating Model Parameters that Memorize Training Examples

Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1879-1888

Implicit Training of Inference Network Models for Structured Prediction

Shiv Shankar; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1889-1899

Efficiently learning the graph for semi-supervised learning

Dravyansh Sharma, Maxwell Jones; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1900-1910

Counting Background Knowledge Consistent Markov Equivalent Directed Acyclic Graphs

Vidya Sagar Sharma; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1911-1920

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

Vishal Sharma, Daman Arora, , Parag Singla; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1921-1931

Risk-limiting financial audits via weighted sampling without replacement

Shubhanshu Shekhar, Ziyu Xu, Zachary Lipton, Pierre Liang, Aaditya Ramdas; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1932-1941

Learning Nonlinear Causal Effect via Kernel Anchor Regression

Wenqi Shi, Wenkai Xu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1942-1952

A Bayesian approach for bandit online optimization with switching cost

Zai Shi, Jian Tan, Feifei Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1953-1963

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

Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1964-1973

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

Joar Skalse, Alessandro Abate; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1974-1984

Aligned Diffusion Schrödinger Bridges

Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, Maria Rodriguez Martinez, Andreas Krause, Charlotte Bunne; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1985-1995

ViBid: Linear Vision Transformer with Bidirectional Normalization

Jeonggeun Song, Heung-Chang Lee; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1996-2005

Fast Heterogeneous Federated Learning with Hybrid Client Selection

Duanxiao Song, Guangyuan Shen, Dehong Gao, Libin Yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2006-2015

Solving multi-model MDPs by coordinate ascent and dynamic programming

Xihong Su, Marek Petrik; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2016-2025

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

Jinyan Su, Changhong Zhao, Di Wang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2026-2035

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2036-2046

Meta-learning Control Variates: Variance Reduction with Limited Data

Zhuo Sun, Chris J Oates, François-Xavier Briol; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2047-2057

Pandering in a (flexible) representative democracy

Xiaolin Sun, Jacob Masur, Ben Abramowitz, Nicholas Mattei, Zizhan Zheng; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2058-2068

Locally Regularized Sparse Graph by Fast Proximal Gradient Descent

Dongfang Sun, Yingzhen Yang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2069-2077

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

Kamil Szyc, Tomasz Walkowiak, Henryk Maciejewski; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2078-2088

Exploiting Inferential Structure in Neural Processes

Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric Nalisnick; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2089-2098

Two-stage Kernel Bayesian Optimization in High Dimensions

Jian Tan, Niv Nayman; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2099-2110

Low-rank matrix recovery with unknown correspondence

Zhiwei Tang, Tsung-Hui Chang, Xiaojing Ye, Hongyuan Zha; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2111-2122

Fairness-aware class imbalanced learning on multiple subgroups

Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, Li Shen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2123-2133

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2134-2146

Bandits with costly reward observations

Aaron D. Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2147-2156

Probabilistic circuits that know what they don’t know

Fabrizio Ventola, Steven Braun, Yu Zhongjie, Martin Mundt, Kristian Kersting; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2157-2167

A policy gradient approach for optimization of smooth risk measures

Nithia Vijayan, L. A. Prashanth; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2168-2178

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

Yoav Wald, Suchi Saria; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2179-2191

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2192-2202

Efficient Privacy-Preserving Stochastic Nonconvex Optimization

Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2203-2213

Diversity-enhanced probabilistic ensemble for uncertainty estimation

Hanjing Wang, Qiang Ji; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2214-2225

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

Yiqi Wang, Mengdi Xu, Laixi Shi, Yuejie Chi; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2226-2236

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2237-2247

A constrained Bayesian approach to out-of-distribution prediction

Ziyu Wang, Binjie Yuan, Jiaxun Lu, Bowen Ding, Yunfeng Shao, Qibin Wu, Jun Zhu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2248-2258

On the Role of Generalization in Transferability of Adversarial Examples

Yilin Wang, Farzan Farnia; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2259-2270

Bidirectional Attention as a Mixture of Continuous Word Experts

Kevin C. Wibisono, Yixin Wang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2271-2281

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2282-2292

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

Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2293-2303

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

Yue Wu, Jiafan He, Quanquan Gu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2304-2313

Robust Quickest Change Detection for Unnormalized Models

Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2314-2323

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

Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2324-2334

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2335-2345

Conformal Risk Control for Ordinal Classification

Yunpeng Xu, Wenge Guo, Zhi Wei; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2346-2355

$E(2)$-Equivariant Vision Transformer

Renjun Xu, Kaifan Yang, Ke Liu, Fengxiang He; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2356-2366

Provably Efficient Adversarial Imitation Learning with Unknown Transitions

Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2367-2378

Pessimistic Model Selection for Offline Deep Reinforcement Learning

Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, Pin-Yu Chen; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2379-2389

Mixture of Normalizing Flows for European Option Pricing

Yongxin Yang, Timothy M. Hospedales; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2390-2399

Multi-modal differentiable unsupervised feature selection

Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2400-2410

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

Chengmei Yang, Bowei He, Yimeng Wu, Chao Xing, Lianghua He, Chen Ma; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2411-2421

Mitigating Transformer Overconfidence via Lipschitz Regularization

Wenqian Ye, Yunsheng Ma, Xu Cao, Kun Tang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2422-2432

Towards Physically Reliable Molecular Representation Learning

Seunghoon Yi, Youngwoo Cho, Jinhwan Sul, Seung Woo Ko, Soo Kyung Kim, Jaegul Choo, Hongkee Yoon, Joonseok Lee; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2433-2443

Monte-Carlo Search for an Equilibrium in Dec-POMDPs

Yang You, Vincent Thomas, Francis Colas, Olivier Buffet; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2444-2453

Online estimation of similarity matrices with incomplete data

Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2454-2464

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2465-2476

Energy-based Predictive Representations for Partially Observed Reinforcement Learning

Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2477-2487

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2488-2497

Graph Self-supervised Learning via Proximity Distribution Minimization

Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2498-2508

Greed is good: correspondence recovery for unlabeled linear regression

Hang Zhang, Ping Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2509-2518

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2519-2528

Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning

Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2529-2540

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

Tianhang Zheng, Baochun Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2541-2550

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

Qi Zhou, Jie Wang, Qiyuan Liu, Yufei Kuang, Wengang Zhou, Houqiang Li; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2551-2562

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

Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2563-2573

AUC Maximization in Imbalanced Lifelong Learning

Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2574-2585

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

Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2586-2596

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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2597-2607

Regularized online DR-submodular optimization

Pengyu Zuo, Yao Wang, Shaojie Tang; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2608-2617

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