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Editors: Robin J. Evans, Ilya Shpitser
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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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1-10
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
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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
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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
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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
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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
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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; 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
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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
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
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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; 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 Raedt; 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
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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; 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
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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; 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
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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
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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
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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
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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
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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
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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; 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
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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
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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
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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
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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
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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
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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
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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
Bayesian 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
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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
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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; 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 Hüllermeier; 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, Mausam , 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
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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
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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
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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, Zhongjie Yu, 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
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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; 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
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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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2335-2345
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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
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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
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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
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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; Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2597-2607
Regularized online DR-submodular optimization
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