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Editors: Cassio de Campos, Marloes H. Maathuis
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Proceedings of the thirty-seventh conference on Uncertainty in Artificial Intelligence — Preface
; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1-11
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The neural moving average model for scalable variational inference of state space models
Thomas Ryder, Dennis Prangle, Andrew Golightly, Isaac Matthews; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:12-22
Task similarity aware meta learning: theory-inspired improvement on MAML
Pan Zhou, Yingtian Zou, Xiao-Tong Yuan, Jiashi Feng, Caiming Xiong, Steven Hoi; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:23-33
Efficient debiased evidence estimation by multilevel Monte Carlo sampling
Kei Ishikawa, Takashi Goda; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:34-43
Variational inference with continuously-indexed normalizing flows
Anthony Caterini, Rob Cornish, Dino Sejdinovic, Arnaud Doucet; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:44-53
TreeBERT: A tree-based pre-trained model for programming language
Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, Lei Lyu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:54-63
Competitive policy optimization
Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:64-74
Improving uncertainty calibration of deep neural networks via truth discovery and geometric optimization
Chunwei Ma, Ziyun Huang, Jiayi Xian, Mingchen Gao, Jinhui Xu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:75-85
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation
Takeshi Teshima, Masashi Sugiyama; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:86-96
Causal additive models with unobserved variables
Takashi Nicholas Maeda, Shohei Shimizu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:97-106
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A variational approximation for analyzing the dynamics of panel data
Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya Ravi, Vikas Singh; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:107-117
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Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks
Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande, Vikas Singh; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:118-128
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The curious case of adversarially robust models: More data can help, double descend, or hurt generalization
Yifei Min, Lin Chen, Amin Karbasi; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:129-139
Contrastive prototype learning with augmented embeddings for few-shot learning
Yizhao Gao, Nanyi Fei, Guangzhen Liu, Zhiwu Lu, Tao Xiang; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:140-150
XOR-SGD: provable convex stochastic optimization for decision-making under uncertainty
Fan Ding, Yexiang Xue; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:151-160
Path dependent structural equation models
Ranjani Srinivasan, Jaron J. R. Lee, Rohit Bhattacharya, Ilya Shpitser; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:161-171
Featurized density ratio estimation
Kristy Choi, Madeline Liao, Stefano Ermon; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:172-182
Variance reduction in frequency estimators via control variates method
Rameshwar Pratap, Raghav Kulkarni; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:183-193
Application of kernel hypothesis testing on set-valued data
Alexis Bellot, Mihaela van der Schaar; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:194-204
A kernel two-sample test with selection bias
Alexis Bellot, Mihaela van der Schaar; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:205-214
An unsupervised video game playstyle metric via state discretization
Chiu-Chou Lin, Wei-Chen Chiu, I-Chen Wu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:215-224
Most: multi-source domain adaptation via optimal transport for student-teacher learning
Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Phung; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:225-235
Constrained labeling for weakly supervised learning
Chidubem Arachie, Bert Huang; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:236-246
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Communication efficient parallel reinforcement learning
Mridul Agarwal, Bhargav Ganguly, Vaneet Aggarwal; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:247-256
Robust reinforcement learning under minimax regret for green security
Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:257-267
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Defending SVMs against poisoning attacks: the hardness and DBSCAN approach
Hu Ding, Fan Yang, Jiawei Huang; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:268-278
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Matrix games with bandit feedback
Brendan O’Donoghue, Tor Lattimore, Ian Osband; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:279-289
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Improving approximate optimal transport distances using quantization
Gaspard Beugnot, Aude Genevay, Kristjan Greenewald, Justin Solomon; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:290-300
Approximate implication with d-separation
Batya Kenig; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:301-311
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Hierarchical probabilistic model for blind source separation via Legendre transformation
Simon Luo, Lamiae Azizi, Mahito Sugiyama; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:312-321
Lifted reasoning meets weighted model integration
Jonathan Feldstein, Vaishak Belle; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:322-332
Formal verification of neural networks for safety-critical tasks in deep reinforcement learning
Davide Corsi, Enrico Marchesini, Alessandro Farinelli; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:333-343
Learnable uncertainty under Laplace approximations
Agustinus Kristiadi, Matthias Hein, Philipp Hennig; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:344-353
Symmetric Wasserstein autoencoders
Sun Sun, Hongyu Guo; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:354-364
Unsupervised anomaly detection with adversarial mirrored autoencoders
Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:365-375
Action redundancy in reinforcement learning
Nir Baram, Guy Tennenholtz, Shie Mannor; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:376-385
Weighted model counting with conditional weights for Bayesian networks
Paulius Dilkas, Vaishak Belle; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:386-396
Escaping from zero gradient: Revisiting action-constrained reinforcement learning via Frank-Wolfe policy optimization
Jyun-Li Lin, Wei Hung, Shang-Hsuan Yang, Ping-Chun Hsieh, Xi Liu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:397-407
Unsupervised program synthesis for images by sampling without replacement
Chenghui Zhou, Chun-Liang Li, Barnabás Póczos; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:408-418
On the distributional properties of adaptive gradients
Zhiyi Zhang, Ziyin Liu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:419-429
Bandits with partially observable confounded data
Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:430-439
Structured sparsification with joint optimization of group convolution and channel shuffle
Xin-Yu Zhang, Kai Zhao, Taihong Xiao, Ming-Ming Cheng, Ming-Hsuan Yang; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:440-450
A weaker faithfulness assumption based on triple interactions
Alexander Marx, Arthur Gretton, Joris M. Mooij; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:451-460
pRSL: Interpretable multi-label stacking by learning probabilistic rules
Michael Kirchhof, Lena Schmid, Christopher Reining, Michael ten Hompel, Markus Pauly; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:461-470
Regstar: efficient strategy synthesis for adversarial patrolling games
David Klaška, Antonín Kučera, Vít Musil, Vojtěch Řehák; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:471-481
The complexity of nonconvex-strongly-concave minimax optimization
Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:482-492
High-dimensional Bayesian optimization with sparse axis-aligned subspaces
David Eriksson, Martin Jankowiak; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:493-503
Known unknowns: Learning novel concepts using reasoning-by-elimination
Harsh Agrawal, Eli A. Meirom, Yuval Atzmon, Shie Mannor, Gal Chechik; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:504-514
Dynamic visualization for L1 fusion convex clustering in near-linear time
Bingyuan Zhang, Jie Chen, Yoshikazu Terada; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:515-524
FlexAE: flexibly learning latent priors for wasserstein auto-encoders
Arnab Kumar Mondal, Himanshu Asnani, Parag Singla, AP Prathosh; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:525-535
Generalized parametric path problems
Kshitij Gajjar, Girish Varma, Prerona Chatterjee, Jaikumar Radhakrishnan; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:536-546
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Efficient greedy coordinate descent via variable partitioning
Huang Fang, Guanhua Fang, Tan Yu, Ping Li; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:547-557
Bayesian streaming sparse Tucker decomposition
Shikai Fang, Robert M. Kirby, Shandian Zhe; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:558-567
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:568-577
Asynchronous $ε$-Greedy Bayesian Optimisation
George De Ath, Richard M. Everson, Jonathan E. Fieldsend; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:578-588
Global explanations with decision rules: a co-learning approach
Géraldin Nanfack, Paul Temple, Benoît Frénay; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:589-599
Addressing fairness in classification with a model-agnostic multi-objective algorithm
Kirtan Padh, Diego Antognini, Emma Lejal-Glaude, Boi Faltings, Claudiu Musat; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:600-609
A unifying framework for observer-aware planning and its complexity
Shuwa Miura, Shlomo Zilberstein; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:610-620
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A heuristic for statistical seriation
Komal Dhull, Jingyan Wang, Nihar B. Shah, Yuanzhi Li, R. Ravi; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:621-631
LocalNewton: Reducing communication rounds for distributed learning
Vipul Gupta, Avishek Ghosh, Michał Dereziński, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:632-642
Generative Archimedean copulas
Yuting Ng, Ali Hasan, Khalil Elkhalil, Vahid Tarokh; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:643-653
Exploring the loss landscape in neural architecture search
Colin White, Sam Nolen, Yash Savani; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:654-664
Finite-time theory for momentum Q-learning
Weng Bowen, Xiong Huaqing, Zhao Lin, Liang Yingbin, Zhang Wei; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:665-674
Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting
Adam D. Cobb, Brian Jalaian; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:675-685
Robust principal component analysis for generalized multi-view models
Frank Nussbaum, Joachim Giesen; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:686-695
Decentralized multi-agent active search for sparse signals
Ramina Ghods, Arundhati Banerjee, Jeff Schneider; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:696-706
Unbiased gradient estimation for variational auto-encoders using coupled Markov chains
Francisco J. R. Ruiz, Michalis K. Titsias, Taylan Cemgil, Arnaud Doucet; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:707-717
Possibilistic preference elicitation by minimax regret
Loïc Adam, Sebastien Destercke; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:718-727
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When is particle filtering efficient for planning in partially observed linear dynamical systems?
Simon S. Du, Wei Hu, Zhiyuan Li, Ruoqi Shen, Zhao Song, Jiajun Wu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:728-737
Thompson sampling for Markov games with piecewise stationary opponent policies
Anthony DiGiovanni, Ambuj Tewari; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:738-748
Hierarchical Indian buffet neural networks for Bayesian continual learning
Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen J. Roberts; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:749-759
Measuring data leakage in machine-learning models with Fisher information
Awni Hannun, Chuan Guo, Laurens van der Maaten; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:760-770
Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces
Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:771-780
Probabilistic task modelling for meta-learning
Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:781-791
Approximation algorithm for submodular maximization under submodular cover
Naoto Ohsaka, Tatsuya Matsuoka; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:792-801
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Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms
Fengxiang He, Bohan Wang, Dacheng Tao; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:802-812
Dependency in DAG models with hidden variables
Robin J. Evans; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:813-822
Natural language adversarial defense through synonym encoding
Xiaosen Wang, Jin Hao, Yichen Yang, Kun He; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:823-833
Path-BN: Towards effective batch normalization in the Path Space for ReLU networks
Xufang Luo, Qi Meng, Wei Chen, Yunhong Wang, Tie-Yan Liu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:834-843
Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev, Aaditya Ramdas; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:844-853
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern Eskofier; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:854-864
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Combinatorial semi-bandit in the non-stationary environment
Wei Chen, Liwei Wang, Haoyu Zhao, Kai Zheng; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:865-875
Time-variant variational transfer for value functions
Giuseppe Canonaco, Andrea Soprani, Matteo Giuliani, Andrea Castelletti, Manuel Roveri, Marcello Restelli; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:876-886
BayLIME: Bayesian local interpretable model-agnostic explanations
Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, David Flynn; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:887-896
On random kernels of residual architectures
Etai Littwin, Tomer Galanti, Lior Wolf; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:897-907
Neural markov logic networks
Giuseppe Marra, Ondřej Kuželka; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:908-917
Deep kernels with probabilistic embeddings for small-data learning
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin Han; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:918-928
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On the effects of quantisation on model uncertainty in Bayesian neural networks
Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:929-938
GP-ConvCNP: Better generalization for conditional convolutional Neural Processes on time series data
Jens Petersen, Gregor Köhler, David Zimmerer, Fabian Isensee, Paul F. Jäger, Klaus H. Maier-Hein; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:939-949
Mixed variable Bayesian optimization with frequency modulated kernels
Changyong Oh, Efstratios Gavves, Max Welling; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:950-960
Subseasonal climate prediction in the western US using Bayesian spatial models
Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:961-970
variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference
Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David Blei, Itsik Pe’er; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:971-981
Estimating treatment effects with observed confounders and mediators
Shantanu Gupta, Zachary C. Lipton, David Childers; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:982-991
No-regret learning with high-probability in adversarial Markov decision processes
Mahsa Ghasemi, Abolfazl Hashemi, Haris Vikalo, Ufuk Topcu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:992-1001
A decentralized policy gradient approach to multi-task reinforcement learning
Sihan Zeng, Malik Aqeel Anwar, Thinh T. Doan, Arijit Raychowdhury, Justin Romberg; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1002-1012
Compositional abstraction error and a category of causal models
Eigil F. Rischel, Sebastian Weichwald; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1013-1023
Bayesian optimization for modular black-box systems with switching costs
Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1024-1034
Probabilistic selection of inducing points in sparse Gaussian processes
Anders Kirk Uhrenholt, Valentin Charvet, Bjørn Sand Jensen; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1035-1044
Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables
Noam Finkelstein, Beata Zjawin, Elie Wolfe, Ilya Shpitser, Robert W. Spekkens; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1045-1055
Learning proposals for probabilistic programs with inference combinators
Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem van de Meent; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1056-1066
Hierarchical infinite relational model
Feras A. Saad, Vikash K. Mansinghka; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1067-1077
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Unsupervised constrained community detection via self-expressive graph neural network
Sambaran Bandyopadhyay, Vishal Peter; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1078-1088
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NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation
Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1089-1099
PALM: Probabilistic area loss Minimization for Protein Sequence Alignment
Fan Ding, Nan Jiang, Jianzhu Ma, Jian Peng, Jinbo Xu, Yexiang Xue; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1100-1109
Principal component analysis in the stochastic differential privacy model
Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1110-1119
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Variance-dependent best arm identification
Pinyan Lu, Chao Tao, Xiaojin Zhang; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1120-1129
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Stochastic continuous normalizing flows: training SDEs as ODEs
Liam Hodgkinson, Chris van der Heide, Fred Roosta, Michael W. Mahoney; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1130-1140
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On the distribution of penultimate activations of classification networks
Minkyo Seo, Yoonho Lee, Suha Kwak; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1141-1151
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Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling
Difan Zou, Pan Xu, Quanquan Gu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1152-1162
Tractable computation of expected kernels
Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1163-1173
Sparse linear networks with a fixed butterfly structure: theory and practice
Nir Ailon, Omer Leibovitch, Vineet Nair; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1174-1184
Uncertainty-aware sensitivity analysis using Rényi divergences
Topi Paananen, Michael Riis Andersen, Aki Vehtari; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1185-1194
Testification of Condorcet Winners in dueling bandits
Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1195-1205
The promises and pitfalls of deep kernel learning
Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1206-1216
Confidence in causal discovery with linear causal models
David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1217-1226
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Classification with abstention but without disparities
Nicolas Schreuder, Evgenii Chzhen; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1227-1236
Maximal ancestral graph structure learning via exact search
Kari Rantanen, Antti Hyttinen, Matti Järvisalo; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1237-1247
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Extendability of causal graphical models: Algorithms and computational complexity
Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1248-1257
Gaussian process nowcasting: application to COVID-19 mortality reporting
Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra, Xenia Miscouridou, Ricardo P Schnekenberg, Charles Whittaker, Michaela Vollmer, Seth Flaxman, Samir Bhatt, Thomas A. Mellan; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1258-1268
Trumpets: Injective flows for inference and inverse problems
Konik Kothari, AmirEhsan Khorashadizadeh, Maarten de Hoop, Ivan Dokmanić; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1269-1278
Stochastic model for sunk cost bias
Jon Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1279-1288
Optimized auxiliary particle filters: adapting mixture proposals via convex optimization
Nicola Branchini, Víctor Elvira; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1289-1299
Inference of causal effects when control variables are unknown
Ludvig Hult, Dave Zachariah; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1300-1309
Dimension reduction for data with heterogeneous missingness
Yurong Ling, Zijing Liu, Jing-Hao Xue; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1310-1320
Tensor-train density estimation
Georgii S. Novikov, Maxim E. Panov, Ivan V. Oseledets; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1321-1331
Similarity measure for sparse time course data based on Gaussian processes
Zijing Liu, Mauricio Barahona; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1332-1341
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Towards robust episodic meta-learning
Beyza Ermis, Giovanni Zappella, Cédric Archambeau; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1342-1351
ReZero is all you need: fast convergence at large depth
Thomas Bachlechner, Bodhisattwa Prasad Majumder, Henry Mao, Gary Cottrell, Julian McAuley; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1352-1361
Subset-of-data variational inference for deep Gaussian-processes regression
Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1362-1370
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components
Andrew H. Song, Demba Ba, Emery N. Brown; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1371-1381
Local explanations via necessity and sufficiency: unifying theory and practice
David S. Watson, Limor Gultchin, Ankur Taly, Luciano Floridi; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1382-1392
Faster lifting for two-variable logic using cell graphs
Timothy van Bremen, Ondřej Kuželka; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1393-1402
Post-hoc loss-calibration for Bayesian neural networks
Meet P. Vadera, Soumya Ghosh, Kenney Ng, Benjamin M. Marlin; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1403-1412
Towards tractable optimism in model-based reinforcement learning
Aldo Pacchiano, Philip Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1413-1423
Probabilistic DAG search
Julia Grosse, Cheng Zhang, Philipp Hennig; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1424-1433
Causal and interventional Markov boundaries
Sofia Triantafillou, Fattaneh Jabbari, Gregory F. Cooper; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1434-1443
Simple combinatorial algorithms for combinatorial bandits: corruptions and approximations
Haike Xu, Jian Li; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1444-1454
CLAIM: curriculum learning policy for influence maximization in unknown social networks
Dexun Li, Meghna Lowalekar, Pradeep Varakantham; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1455-1465
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Learning to learn with Gaussian processes
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1466-1475
Sum-product laws and efficient algorithms for imprecise Markov chains
Jasper De Bock, Alexander Erreygers, Thomas Krak; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1476-1485
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Trusted-maximizers entropy search for efficient Bayesian optimization
Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1486-1495
Minimax sample complexity for turn-based stochastic game
Qiwen Cui, Lin F. Yang; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1496-1504
Multi-output Gaussian Processes for uncertainty-aware recommender systems
Yinchong Yang, Florian Buettner; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1505-1514
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Generalization error bounds for deep unfolding RNNs
Boris Joukovsky, Tanmoy Mukherjee, Huynh Van Luong, Nikos Deligiannis; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1515-1524
RISAN: Robust instance specific deep abstention network
Bhavya Kalra, Kulin Shah, Naresh Manwani; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1525-1534
Contingency-aware influence maximization: A reinforcement learning approach
Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, Milind Tambe; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1535-1545
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Invariant representation learning for treatment effect estimation
Claudia Shi, Victor Veitch, David M. Blei; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1546-1555
Generating adversarial examples with graph neural networks
Florian Jaeckle, M. Pawan Kumar; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1556-1564
A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery
Philip A. Boeken, Joris M. Mooij; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1565-1575
Graph-based semi-supervised learning through the lens of safety
Shreyas Sheshadri, Avirup Saha, Priyank Patel, Samik Datta, Niloy Ganguly; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1576-1586
Strategically efficient exploration in competitive multi-agent reinforcement learning
Robert Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1587-1596
Information theoretic meta learning with Gaussian processes
Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1597-1606
Combining pseudo-point and state space approximations for sum-separable Gaussian Processes
Will Tebbutt, Arno Solin, Richard E. Turner; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1607-1617
Class balancing GAN with a classifier in the loop
Harsh Rangwani, Konda Reddy Mopuri, R. Venkatesh Babu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1618-1627
Hierarchical learning of Hidden Markov Models with clustering regularization
Hui Lan, Antoni B. Chan; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1628-1638
Enabling long-range exploration in minimization of multimodal functions
Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1639-1649
An optimization and generalization analysis for max-pooling networks
Alon Brutzkus, Amir Globerson; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1650-1660
Investigating vulnerabilities of deep neural policies
Ezgi Korkmaz; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1661-1670
Modeling financial uncertainty with multivariate temporal entropy-based curriculums
Ramit Sawhney, Arnav Wadhwa, Ayush Mangal, Vivek Mittal, Shivam Agarwal, Rajiv Ratn Shah; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1671-1681
Random probabilistic circuits
Nicola Di Mauro, Gennaro Gala, Marco Iannotta, Teresa M.A. Basile; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1682-1691
Multi-task and meta-learning with sparse linear bandits
Leonardo Cella, Massimiliano Pontil; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1692-1702
Federated stochastic gradient Langevin dynamics
Khaoula el Mekkaoui, Diego Mesquita, Paul Blomstedt, Samuel Kaski; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1703-1712
Certification of iterative predictions in Bayesian neural networks
Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1713-1723
Integer programming-based error-correcting output code design for robust classification
Samarth Gupta, Saurabh Amin; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1724-1734
Statistically robust neural network classification
Benjie Wang, Stefan Webb, Tom Rainforth; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1735-1745
Markov equivalence of max-linear Bayesian networks
Carlos Améndola, Benjamin Hollering, Seth Sullivant, Ngoc Tran; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1746-1755
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Constrained differentially private federated learning for low-bandwidth devices
Raouf Kerkouche, Gergely Ács, Claude Castelluccia, Pierre Genevès; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1756-1765
Know your limits: Uncertainty estimation with ReLU classifiers fails at reliable OOD detection
Dennis Ulmer, Giovanni Cinà; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1766-1776
Nearest neighbor search under uncertainty
Blake Mason, Ardhendu Tripathy, Robert Nowak; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1777-1786
Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1787-1797
Partial Identifiability in Discrete Data with Measurement Error
Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1798-1808
Bias-corrected peaks-over-threshold estimation of the CVaR
Dylan Troop, Frédéric Godin, Jia Yuan Yu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1809-1818
Variational refinement for importance sampling using the forward Kullback-Leibler divergence
Ghassen Jerfel, Serena Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1819-1829
Diagnostics for conditional density models and Bayesian inference algorithms
David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1830-1840
Non-PSD matrix sketching with applications to regression and optimization
Zhili Feng, Fred Roosta, David P. Woodruff; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1841-1851
Staying in shape: learning invariant shape representations using contrastive learning
Jeffrey Gu, Serena Yeung; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1852-1862
Convergence behavior of belief propagation: estimating regions of attraction via Lyapunov functions
Harald Leisenberger, Christian Knoll, Richard Seeber, Franz Pernkopf; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1863-1873
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Explaining fast improvement in online imitation learning
Xinyan Yan, Byron Boots, Ching-An Cheng; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1874-1884
Gradient-based optimization for multi-resource spatial coverage problems
Nitin Kamra, Yan Liu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1885-1894
Doubly non-central beta matrix factorization for DNA methylation data
Aaron Schein, Anjali Nagulpally, Hanna Wallach, Patrick Flaherty; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1895-1904
SGD with low-dimensional gradients with applications to private and distributed learning
Shiva Prasad Kasiviswanathan; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1905-1915
Active multi-fidelity Bayesian online changepoint detection
Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1916-1926
Learning in Multi-Player Stochastic Games
William Brown; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1927-1937
q-Paths: Generalizing the geometric annealing path using power means
Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1938-1947
Condition number bounds for causal inference
Spencer L. Gordon, Vinayak M. Kumar, Leonard J. Schulman, Piyush Srivastava; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1948-1957
Sketching curvature for efficient out-of-distribution detection for deep neural networks
Apoorva Sharma, Navid Azizan, Marco Pavone; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1958-1967
CORe: Capitalizing On Rewards in Bandit Exploration
Nan Wang, Branislav Kveton, Maryam Karimzadehgan; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1968-1978
Explicit pairwise factorized graph neural network for semi-supervised node classification
Yu Wang, Yuesong Shen, Daniel Cremers; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1979-1987
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Statistical mechanical analysis of neural network pruning
Rupam Acharyya, Ankani Chattoraj, Boyu Zhang, Shouman Das, Daniel Štefankovič; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1988-1997
Correlated weights in infinite limits of deep convolutional neural networks
Adrià Garriga-Alonso, Mark van der Wilk; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1998-2007
Leveraging probabilistic circuits for nonparametric multi-output regression
Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2008-2018
PROVIDE: a probabilistic framework for unsupervised video decomposition
Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal, Andreas M. Lehrmann; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2019-2028
Uncertainty in minimum cost multicuts for image and motion segmentation
Amirhossein Kardoost, Margret Keuper; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2029-2038
Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging
Renato Lui Geh, Denis Deratani Mauá; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2039-2049
Conditionally independent data generation
Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2050-2060
Exact and approximate hierarchical clustering using A*
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2061-2071
Efficient online inference for nonparametric mixture models
Rylan Schaeffer, Blake Bordelon, Mikail Khona, Weiwei Pan, Ila Rani Fiete; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2072-2081
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No-regret approximate inference via Bayesian optimisation
Rafael Oliveira, Lionel Ott, Fabio Ramos; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2082-2092
Disentangling mixtures of unknown causal interventions
Abhinav Kumar, Gaurav Sinha; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2093-2102
SDM-Net: A simple and effective model for generalized zero-shot learning
Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2103-2113
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Towards a unified framework for fair and stable graph representation learning
Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2114-2124
Identifying regions of trusted predictions
Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2125-2134
Learning and certification under instance-targeted poisoning
Ji Gao, Amin Karbasi, Mohammad Mahmoody; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2135-2145
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Min/max stability and box distributions
Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, Andrew McCallum; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2146-2155
Geometric rates of convergence for kernel-based sampling algorithms
Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2156-2164
Sequential core-set Monte Carlo
Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell; Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:2165-2175
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