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ORCID38th UAI 2022: Eindhoven, The Netherlands
- James Cussens, Kun Zhang:
Uncertainty in Artificial Intelligence, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022, 1-5 August 2022, Eindhoven, The Netherlands. Proceedings of Machine Learning Research 180, PMLR 2022 - Kenshi Abe, Mitsuki Sakamoto, Atsushi Iwasaki:
Mutation-driven follow the regularized leader for last-iterate convergence in zero-sum games. 1-10 - Sakshi Agarwal, Kalev Kask, Alexander Ihler, Rina Dechter:
NeuroBE: Escalating neural network approximations of Bucket Elimination. 11-21 - Mridul Agarwal, Qinbo Bai, Vaneet Aggarwal:
Regret guarantees for model-based reinforcement learning with long-term average constraints. 22-31 - Andrea Agiollo, Andrea Omicini:
GNN2GNN: Graph neural networks to generate neural networks. 32-42 - Kareem Ahmed, Eric Wang, Kai-Wei Chang, Guy Van den Broeck:
Neuro-symbolic entropy regularization. 43-53 - Ragib Ahsan, Zahra Fatemi, David Arbour, Elena Zheleva:
Non-parametric inference of relational dependence. 54-63 - Motasem Alfarra, Adel Bibi, Philip H. S. Torr, Bernard Ghanem:
Data dependent randomized smoothing. 64-74 - Tahar Allouche, Jérôme Lang, Florian Yger:
Multi-winner approval voting goes epistemic. 75-84 - Roman Andriushchenko, Milan Ceska, Sebastian Junges
, Joost-Pieter Katoen:
Inductive synthesis of finite-state controllers for POMDPs. 85-95 - Charles K. Assaad, Emilie Devijver, Éric Gaussier:
Discovery of extended summary graphs in time series. 96-106 - Andrea Baisero, Brett Daley, Christopher Amato:
Asymmetric DQN for partially observable reinforcement learning. 107-117 - Tianshu Bao, Shengyu Chen, Taylor T. Johnson, Peyman Givi, Shervin Sammak, Xiaowei Jia:
Physics guided neural networks for spatio-temporal super-resolution of turbulent flows. 118-128 - Yajie Bao, Weidong Liu, Xiaojun Mao, Weijia Xiong:
Byzantine-tolerant distributed multiclass sparse linear discriminant analysis. 129-138 - Sergey Bartunov, Fabian B. Fuchs, Timothy P. Lillicrap:
Equilibrium aggregation: encoding sets via optimization. 139-149 - Tsviel Ben Shabat, Reshef Meir, David Azriel:
Empirical bayes approach to truth discovery problems. 150-158 - Petra Berenbrink, Colin Cooper, Cristina Gava, David Kohan Marzagão, Frederik Mallmann-Trenn, Tomasz Radzik:
On early extinction and the effect of travelling in the SIR model. 159-169 - Michel Besserve, Bernhard Schölkopf:
Learning soft interventions in complex equilibrium systems. 170-180 - Sanjay P. Bhat, Chaitanya Amballa:
Identifying near-optimal decisions in linear-in-parameter bandit models with continuous decision sets. 181-190 - Sujay Bhatt, Guanhua Fang, Ping Li:
Offline change detection under contamination. 191-201 - Rohit Bhattacharya, Razieh Nabi:
On testability of the front-door model via Verma constraints. 202-212 - Tineke Blom, Joris M. Mooij:
Robustness of model predictions under extension. 213-222 - Trevor Bonjour, Vaneet Aggarwal, Bharat K. Bhargava:
Information theoretic approach to detect collusion in multi-agent games. 223-232 - Tanya Braun, Marcel Gehrke, Florian Lau, Ralf Möller:
Lifting in multi-agent systems under uncertainty. 233-243 - Tomás Brázdil, David Klaska, Antonín Kucera, Vít Musil, Petr Novotný, Vojtech Rehák:
On-the-fly adaptation of patrolling strategies in changing environments. 244-254 - Ido Bronstein, Alon Brutzkus, Amir Globerson:
On the inductive bias of neural networks for learning read-once DNFs. 255-265 - Difeng Cai, Yuliang Ji, Huan He, Qiang Ye, Yuanzhe Xi:
AUTM flow: atomic unrestricted time machine for monotonic normalizing flows. 266-274 - Yiting Cao, Chao Lan:
Active approximately metric-fair learning. 275-285 - Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin, Edward Meeds, Ricardo Henao:
Capturing actionable dynamics with structured latent ordinary differential equations. 286-295 - Kamalika Chaudhuri, Chuan Guo, Mike Rabbat:
Privacy-aware compression for federated data analysis. 296-306 - Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
The optimal noise in noise-contrastive learning is not what you think. 307-316 - Mengjing Chen, Pingzhong Tang, Zihe Wang, Shenke Xiao, Xiwang Yang:
A competitive analysis of online failure-aware assignment. 317-325 - John Chen, Samarth Sinha, Anastasios Kyrillidis:
Stackmix: a complementary mix algorithm. 326-335 - Xinyuan Chen, Zhongmei Zhou, Meichun Gao, Daya Shi, Mohd Nizam Husen:
Knowledge representation combining quaternion path integration and depth-wise atrous circular convolution. 336-345 - Qi Chen, Kai Liu, Ruilong Yao, Hu Ding:
Sublinear time algorithms for greedy selection in high dimensions. 346-356 - Mayee F. Chen, Daniel Y. Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, Christopher Ré:
Shoring up the foundations: fusing model embeddings and weak supervision. 357-367 - Yizuo Chen, Adnan Darwiche:
On the definition and computation of causal treewidth. 368-377 - Jinglin Chen, Nan Jiang:
Offline reinforcement learning under value and density-ratio realizability: The power of gaps. 378-388 - Runxiang Cheng, Gargi Balasubramaniam, Yifei He, Yao-Hung Hubert Tsai, Han Zhao:
Greedy modality selection via approximate submodular maximization. 389-399 - Yoichi Chikahara, Makoto Yamada, Hisashi Kashima:
Feature selection for discovering distributional treatment effect modifiers. 400-410 - Kwanghee Choi, Siyeong Lee:
Combating the instability of mutual information-based losses via regularization. 411-421 - Gabriella Chouraqui, Liron Cohen, Gil Einziger, Liel Leman:
A geometric method for improved uncertainty estimation in real-time. 422-432 - Sewhan Chun, Jae Young Lee, Junmo Kim:
Cyclic test time augmentation with entropy weight method. 433-442 - Tom Claassen, Ioan Gabriel Bucur:
Greedy equivalence search in the presence of latent confounders. 443-452 - Nina L. Corvelo Benz, Manuel Gomez Rodriguez:
Counterfactual inference of second Opinions. 453-463 - Zijun Cui, Hanjing Wang, Tian Gao, Kartik Talamadupula, Qiang Ji:
Variational message passing neural network for Maximum-A-Posteriori (MAP) inference. 464-474 - Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
On provably robust meta-Bayesian optimization. 475-485 - Shantanu Das, Swapnil Dhamal, Ganesh Ghalme, Shweta Jain, Sujit Gujar:
Individual fairness in feature-based pricing for monopoly markets. 486-495 - Rudrajit Das, Anish Acharya, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu:
Faster non-convex federated learning via global and local momentum. 496-506 - Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Multi-objective Bayesian optimization over high-dimensional search spaces. 507-517 - Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio:
Bayesian structure learning with generative flow networks. 518-528 - Grace Deng, David S. Matteson:
Bayesian spillover graphs for dynamic networks. 529-538 - Christophe Denis, Charlotte Dion-Blanc, Laure Sansonnet:
Multiclass classification for Hawkes processes. 539-547 - Eyke Hüllermeier, Sébastien Destercke, Mohammad Hossein Shaker:
Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison. 548-557 - Anthony DiGiovanni, Ambuj Tewari:
Balancing adaptability and non-exploitability in repeated games. 559-568 - Or Dinari, Oren Freifeld:
Variational- and metric-based deep latent space for out-of-distribution detection. 569-578 - Or Dinari, Oren Freifeld:
Revisiting DP-Means: fast scalable algorithms via parallelism and delayed cluster creation. 579-588 - Fan Ding, Yexiang Xue:
X-MEN: guaranteed XOR-maximum entropy constrained inverse reinforcement learning. 589-598 - Punit Pankaj Dubey, Bhisham Dev Verma, Rameshwar Pratap, Keegan Kang:
Improving sign-random-projection via count sketch. 599-609 - Chen Dun, Cameron R. Wolfe, Christopher M. Jermaine, Anastasios Kyrillidis:
ResIST: Layer-wise decomposition of ResNets for distributed training. 610-620 - Varun Embar, Sriram Srinivasan, Lise Getoor:
Learning explainable templated graphical models. 621-630 - Hannes Eriksson, Debabrota Basu, Mina Alibeigi, Christos Dimitrakakis:
SENTINEL: taming uncertainty with ensemble based distributional reinforcement learning. 631-640 - Akram Erraqabi, Marlos C. Machado, Mingde Zhao, Sainbayar Sukhbaatar, Alessandro Lazaric, Ludovic Denoyer, Yoshua Bengio:
Temporal abstractions-augmented temporally contrastive learning: An alternative to the Laplacian in RL. 641-651 - John Isak Texas Falk, Carlo Ciliberto, Massimiliano Pontil:
Implicit kernel meta-learning using kernel integral forms. 652-662 - Yassir Fathullah, Mark J. F. Gales:
Self-distribution distillation: efficient uncertainty estimation. 663-673 - Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann:
Sequential algorithmic modification with test data reuse. 674-684 - Sahil Garg, Umang Gupta, Yu Chen, Syamantak Datta Gupta, Yeshaya Adler, Anderson Schneider, Yuriy Nevmyvaka:
Estimating transfer entropy under long ranged dependencies. 685-695 - Sahra Ghalebikesabi, Harry Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes:
Mitigating statistical bias within differentially private synthetic data. 696-705 - Supriyo Ghosh, Laura Wynter, Shiau Hong Lim, Duc Thien Nguyen:
Neural-progressive hedging: Enforcing constraints in reinforcement learning with stochastic programming. 707-717 - Misha Glazunov, Apostolis Zarras:
Do Bayesian variational autoencoders know what they don't know? 718-727 - Jinwoo Go, Tobin Isaac:
Robust expected information gain for optimal Bayesian experimental design using ambiguity sets. 728-737 - Martin Gubri, Maxime Cordy, Mike Papadakis, Yves Le Traon, Koushik Sen:
Efficient and transferable adversarial examples from bayesian neural networks. 738-748 - Soumyajit Gupta, Gurpreet Singh, Raghu Bollapragada, Matthew Lease:
Learning a neural Pareto manifold extractor with constraints. 749-758 - Ali Hasan, Khalil Elkhalil, Yuting Ng, João M. Pereira, Sina Farsiu, Jose H. Blanchet, Vahid Tarokh:
Modeling extremes with d-max-decreasing neural networks. 759-768 - Tobias Hatt, Daniel Tschernutter, Stefan Feuerriegel:
Generalizing off-policy learning under sample selection bias. 769-779 - Keyang He, Prashant Doshi, Bikramjit Banerjee:
Reinforcement learning in many-agent settings under partial observability. 780-789 - Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen:
Variational multiple shooting for Bayesian ODEs with Gaussian processes. 790-799 - Margot Herin, Patrice Perny, Nataliya Sokolovska:
Learning sparse representations of preferences within Choquet expected utility theory. 800-810 - Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Oluwasanmi Koyejo:
Quadratic metric elicitation for fairness and beyond. 811-821 - Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig:
Fast predictive uncertainty for classification with Bayesian deep networks. 822-832 - Haruo Hosoya:
CIGMO: Categorical invariant representations in a deep generative framework. 833-843 - Bingshan Hu, Nidhi Hegde:
Near-optimal Thompson sampling-based algorithms for differentially private stochastic bandits. 844-852 - Kexin Huang, Vishnu Sresht, Brajesh K. Rai, Mykola Bordyuh:
Uncertainty-aware pseudo-labeling for quantum calculations. 853-862 - Zhipeng Huang, Hadeel Soliman, Subhadeep Paul, Kevin S. Xu:
A mutually exciting latent space Hawkes process model for continuous-time networks. 863-873 - Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen:
Binary independent component analysis: a non-stationarity-based approach. 874-884 - Jacob Imola, Shiva Prasad Kasiviswanathan, Stephen White, Abhinav Aggarwal, Nathanael Teissier:
Balancing utility and scalability in metric differential privacy. 885-894 - Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesváari, Doina Precup:
Towards painless policy optimization for constrained MDPs. 895-905 - Divyansh Jhunjhunwala, Pranay Sharma, Aushim Nagarkatti, Gauri Joshi:
Fedvarp: Tackling the variance due to partial client participation in federated learning. 906-916 - Hongwei Jin, Zishun Yu, Xinhua Zhang:
Orthogonal Gromov-Wasserstein discrepancy with efficient lower bound. 917-927 - Haydn T. Jones, Jacob M. Springer, Garrett T. Kenyon, Juston S. Moore:
If you've trained one you've trained them all: inter-architecture similarity increases with robustness. 928-937 - Anirudh Kakarlapudi, Gayathri Anil, Adam Eck, Prashant Doshi, Leen-Kiat Soh:
Decision-theoretic planning with communication in open multiagent systems. 938-948 - Krishna Chaitanya Kalagarla, Kartik Dhruva, Dongming Shen, Rahul Jain, Ashutosh Nayyar, Pierluigi Nuzzo:
Optimal control of partially observable Markov decision processes with finite linear temporal logic constraints. 949-958 - Vathy M. Kamulete:
Test for non-negligible adverse shifts. 959-968 - Adam Karczmarz, Tomasz P. Michalak, Anish Mukherjee, Piotr Sankowski, Piotr Wygocki:
Improved feature importance computation for tree models based on the Banzhaf value. 969-979 - Ian A. Kash, Zhongkai Wen, Lenore D. Zuck:
Dynamic relocation in ridesharing via fixpoint construction. 980-989 - Jackson A. Killian, Lily Xu, Arpita Biswas, Milind Tambe:
Restless and uncertain: Robust policies for restless bandits via deep multi-agent reinforcement learning. 990-1000 - Jungtaek Kim, Seungjin Choi, Minsu Cho:
Combinatorial Bayesian optimization with random mapping functions to convex polytopes. 1001-1011 - Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion:
On the effectiveness of adversarial training against common corruptions. 1012-1021 - Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami, Negar Kiyavash:
Revisiting the general identifiability problem. 1022-1030 - Thomas Krak:
Hitting times for continuous-time imprecise-Markov chains. 1031-1040 - Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan:
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift. 1041-1051 - Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey:
Greedy relaxations of the sparsest permutation algorithm. 1052-1062 - Richard D. Lange, Ari S. Benjamin, Ralf M. Haefner, Xaq Pitkow:
Interpolating between sampling and variational inference with infinite stochastic mixtures. 1063-1073 - Hyemin Lee, Daijin Kim:
Systematized event-aware learning for multi-object tracking. 1074-1084 - Harald Leisenberger, Franz Pernkopf, Christian Knoll:
Fixing the Bethe approximation: How structural modifications in a graph improve belief propagation. 1085-1095 - Alexander K. Lew, Marco F. Cusumano-Towner, Vikash K. Mansinghka:
Recursive Monte Carlo and variational inference with auxiliary variables. 1096-1106 - Zun Li, Feiran Jia, Aditya Mate, Shahin Jabbari, Mithun Chakraborty, Milind Tambe, Yevgeniy Vorobeychik:
Solving structured hierarchical games using differential backward induction. 1107-1117 - Wenjie Li, Wasif Naeem, Jia Liu, Dequan Zheng, Wei Hao, Lijun Chen:
PDQ-Net: Deep probabilistic dual quaternion network for absolute pose regression on SE(3). 1118-1127 - Mingyang Yi:
Accelerating training of batch normalization: A manifold perspective. 1128-1137 - Naiqi Li, Wenjie Li, Yong Jiang, Shu-Tao Xia:
Deep Dirichlet process mixture models. 1138-1147 - Zihao Li, Xiaohui Bei, Zhenzhen Yan:
Proportional allocation of indivisible resources under ordinal and uncertain preferences. 1148-1157 - Dexun Li, Pradeep Varakantham:
Efficient resource allocation with fairness constraints in restless multi-armed bandits. 1158-1167 - Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha:
A label efficient two-sample test. 1168-1177 - Wanshan Li, Shamindra Shrotriya, Alessandro Rinaldo:
ℓ∞-Bounds of the MLE in the BTL Model under General Comparison Graphs. 1178-1187 - Qiyang Li, Ajay Jain, Pieter Abbeel:
AdaCat: Adaptive categorical discretization for autoregressive models. 1188-1198 - Jakob Lindinger, Barbara Rakitsch, Christoph Lippert:
Laplace approximated Gaussian process state-space models. 1199-1209 - Yurong Ling, Jing-Hao Xue:
Dimension reduction for high-dimensional small counts with KL divergence. 1210-1220 - Xutong Liu, Haoru Zhao, Tong Yu, Shuai Li, John C. S. Lui:
Federated online clustering of bandits. 1221-1231 - Tianyi Liu, Weihao Gao, Zhirui Wang, Chong Wang:
PathFlow: A normalizing flow generator that finds transition paths. 1232-1242 - Zizhen Liu, Si Chen, Jing Ye, Junfeng Fan, Huawei Li, Xiaowei Li:
SASH: Efficient secure aggregation based on SHPRG for federated learning. 1243-1252 - Yao Liu, Yannis Flet-Berliac, Emma Brunskill:
Offline policy optimization with eligible actions. 1253-1263 - Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju:
Data poisoning attacks on off-policy policy evaluation methods. 1264-1274 - Chien Lu, Jaakko Peltonen, Timo Nummenmaa, Jyrki Nummenmaa:
Nonparametric exponential family graph embeddings for multiple representation learning. 1275-1285 - Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone:
Local calibration: metrics and recalibration. 1286-1295 - Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang:
Data sampling affects the complexity of online SGD over dependent data. 1296-1305 - Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson:
Low-precision arithmetic for fast Gaussian processes. 1306-1316 - Pratyush Maini, Xinyun Chen, Bo Li, Dawn Song:
Perturbation type categorization for multiple adversarial perturbation robustness. 1317-1327 - Aurghya Maiti, Vineet Nair, Gaurav Sinha:
A causal bandit approach to learning good atomic interventions in presence of unobserved confounders. 1328-1338 - Anton Matsson, Fredrik D. Johansson:
Case-based off-policy evaluation using prototype learning. 1339-1349 - Lucas Maystre, Tiffany Wu, Roberto Sanchis-Ojeda, Tony Jebara:
Multistate analysis with infinite mixtures of Markov chains. 1350-1359 - Sachit Menon, David M. Blei, Carl Vondrick:
Forget-me-not! Contrastive critics for mitigating posterior collapse. 1360-1370 - Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri:
Can mean field control (mfc) approximate cooperative multi agent reinforcement learning (marl) with non-uniform interaction? 1371-1380 - Joao Monteiro, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, Greg Mori:
Monotonicity regularization: Improved penalties and novel applications to disentangled representation learning and robust classification. 1381-1391 - Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Set-valued prediction in hierarchical classification with constrained representation complexity. 1392-1401