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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 Thomas 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, Dhruva Kartik, 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 - Subhojyoti Mukherjee:
Safety aware changepoint detection for piecewise i.i.d. bandits. 1402-1412 - Subhojyoti Mukherjee, Josiah P. Hanna, Robert D. Nowak:
ReVar: Strengthening policy evaluation via reduced variance sampling. 1413-1422 - Andreas Munk, Berend Zwartsenberg, Adam Scibior, Atilim Günes Baydin, Andrew Stewart, Goran Fernlund, Anoush Poursartip, Frank Wood:
Probabilistic surrogate networks for simulators with unbounded randomness. 1423-1433 - Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison:
Data augmentation in Bayesian neural networks and the cold posterior effect. 1434-1444 - Razieh Nabi, Todd McNutt, Ilya Shpitser:
Semiparametric causal sufficient dimension reduction of multidimensional treatments. 1445-1455 - Hisayoshi Nanmo, Manabu Kuroki:
Partially adaptive regularized multiple regression analysis for estimating linear causal effects. 1456-1465 - Md. Nasim, Xinghang Zhang, Anter El-Azab, Yexiang Xue:
Efficient learning of sparse and decomposable PDEs using random projection. 1466-1476 - Elliot Nelson, Debarun Bhattacharjya, Tian Gao, Miao Liu, Djallel Bouneffouf, Pascal Poupart:
Linearizing contextual bandits with latent state dynamics. 1477-1487 - Daniel Nemirovsky, Nicolas Thiebaut, Ye Xu, Abhishek Gupta:
CounteRGAN: Generating counterfactuals for real-time recourse and interpretability using residual GANs. 1488-1497 - Tuan-Duy H. Nguyen, Ngoc Bui, Duy Nguyen, Man-Chung Yue, Viet Anh Nguyen:
Robust Bayesian recourse. 1498-1508 - Duc Nguyen:
Efficient and accurate top-k recovery from choice data. 1509-1518 - Tuan Nguyen, Van Nguyen, Trung Le, He Zhao, Quan Hung Tran, Dinh Q. Phung:
Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation. 1519-1529 - Yang Ni, Bani K. Mallick:
Ordinal causal discovery. 1530-1540 - Guanyu Nie, Mridul Agarwal, Abhishek Kumar Umrawal, Vaneet Aggarwal, Christopher John Quinn:
An explore-then-commit algorithm for submodular maximization under full-bandit feedback. 1541-1551 - Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Benjamin Van Roy:
Evaluating high-order predictive distributions in deep learning. 1552-1560 - Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand, Martha White, Hengshuai Yao, Mohsen Rohani, Jun Luo:
Understanding and mitigating the limitations of prioritized experience replay. 1561-1571 - Rohith Peddi, Tahrima Rahman, Vibhav Gogate:
Robust learning of tractable probabilistic models. 1572-1581 - Iker Perez, Piotr Skalski, Alec Barns-Graham, Jason Wong, David Sutton:
Attribution of predictive uncertainties in classification models. 1582-1591 - Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider:
Learning large Bayesian networks with expert constraints. 1592-1601 - Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter:
AND/OR branch-and-bound for computational protein design optimizing K. 1602-1612 - Niklas Pfister, Jonas Peters:
Identifiability of sparse causal effects using instrumental variables. 1613-1622 - Victor Picheny, Henry B. Moss, Léeonard Torossian, Nicolas Durrande:
Bayesian quantile and expectile optimisation. 1623-1633 - Daira Pinto Prieto, Ronald de Haan:
Using hierarchies to efficiently combine evidence with Dempster's rule of combination. 1634-1643 - Vladislav Polianskii, Giovanni Luca Marchetti, Alexander Kravberg, Anastasiia Varava, Florian T. Pokorny, Danica Kragic:
Voronoi density estimator for high-dimensional data: Computation, compactification and convergence. 1644-1653 - Mostafa Rahmani:
Clustering a union of linear subspaces via matrix factorization and innovation search. 1654-1664 - Giorgia Ramponi, Marcello Restelli:
Learning in Markov games: Can we exploit a general-sum opponent? 1665-1675 - Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth:
Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently. 1676-1685 - Tongzheng Ren, Tianjun Zhang, Csaba Szepesvári, Bo Dai:
A free lunch from the noise: Provable and practical exploration for representation learning. 1686-1696 - Mathieu Roget, Giuseppe Di Molfetta, Hachem Kadri:
Quantum perceptron revisited: Computational-statistical tradeoffs. 1697-1706 - Esther Rolf, Nikolay Malkin, Alexandros Graikos, Ana Jojic, Caleb Robinson, Nebojsa Jojic:
Resolving label uncertainty with implicit posterior models. 1707-1717 - Maxim Samarin, Volker Roth, David Belius:
Feature learning and random features in standard finite-width convolutional neural networks: An empirical study. 1718-1727 - Karthik Abinav Sankararaman, Anand Louis, Navin Goyal:
Robust identifiability in linear structural equation models of causal inference. 1728-1737 - Amartya Sanyal, Yaxi Hu, Fanny Yang:
How unfair is private learning? 1738-1748 - Pola Schwöbel, Frederik Rahbæk Warburg, Martin Jørgensen, Kristoffer Hougaard Madsen, Søren Hauberg:
Probabilistic spatial transformer networks. 1749-1759 - Kira A. Selby, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart:
Learning functions on multiple sets using multi-set transformers. 1760-1770 - Vishal Sharma, Daman Arora, Florian Geißer, Mausam, Parag Singla:
SymNet 2.0: Effectively handling Non-Fluents and Actions in Generalized Neural Policies for RDDL Relational MDPs. 1771-1781 - Xinwei Shen, Shengyu Zhu, Jiji Zhang, Shoubo Hu, Zhitang Chen:
Reframed GES with a neural conditional dependence measure. 1782-1791 - Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet:
Conditional simulation using diffusion Schrödinger bridges. 1792-1802 - Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low:
Neural ensemble search via Bayesian sampling. 1803-1812 - Egor Shulgin, Peter Richtárik:
Shifted compression framework: generalizations and improvements. 1813-1823 - Anthony Sicilia, Katherine Atwell, Malihe Alikhani, Seong Jae Hwang:
PAC-Bayesian domain adaptation bounds for multiclass learners. 1824-1834 - Sahil Sidheekh, Chris B. Dock, Tushar Jain, Radu V. Balan, Maneesh Kumar Singh:
VQ-Flows: Vector quantized local normalizing flows. 1835-1845 - Jeffrey Smith, Jesse Cranney, Charles Gretton, Damien Gratadour:
Enhanced adaptive optics control with image to image translation. 1846-1856 - Sungryull Sohn, Hyunjae Woo, Jongwook Choi, Lyubing Qiang, Izzeddin Gur, Aleksandra Faust, Honglak Lee:
Fast inference and transfer of compositional task structures for few-shot task generalization. 1857-1865 - Kaiyu Song, Kun Yue, Liang Duan, Mingze Yang, Angsheng Li:
Mutual information based Bayesian graph neural network for few-shot learning. 1866-1875 - Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani:
SMT-based weighted model integration with structure awareness. 1876-1885 - Zehao Su, Leonard Henckel:
A robustness test for estimating total effects with covariate adjustment. 1886-1895 - Daiki Suehiro, Eiji Takimoto:
Simplified and unified analysis of various learning problems by reduction to Multiple-Instance Learning. 1896-1906 - Prasanth Sengadu Suresh, Prashant Doshi:
Marginal MAP estimation for inverse RL under occlusion with observer noise. 1907-1916 - Tongyi Tang, Krishna Balasubramanian, Thomas Chun Man Lee:
High-probability bounds for robust stochastic Frank-Wolfe algorithm. 1917-1927 - Zhiyang Teng, Chenhua Chen, Yan Zhang, Yue Zhang:
Contrastive latent variable models for neural text generation. 1928-1938 - Alexandru Tifrea, Eric Stavarache, Fanny Yang:
Semi-supervised novelty detection using ensembles with regularized disagreement. 1939-1948 - Federico Tomasi, Mounia Lalmas, Zhenwen Dai:
Efficient inference for dynamic topic modeling with large vocabularies. 1950-1959 - Daniele Tramontano, Anthea Monod, Mathias Drton:
Learning linear non-Gaussian polytree models. 1960-1969 - Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil:
Multi-source domain adaptation via weighted joint distributions optimal transport. 1970-1980 - Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk:
Towards unsupervised open world semantic segmentation. 1981-1991 - Tycho F. A. van der Ouderaa, Mark van der Wilk:
Learning invariant weights in neural networks. 1992-2001 - Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing:
Causal forecasting: generalization bounds for autoregressive models. 2002-2012 - Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Intervention target estimation in the presence of latent variables. 2013-2023 - Thanh Vinh Vo, Young Lee, Trong Nghia Hoang, Tze-Yun Leong:
Bayesian federated estimation of causal effects from observational data. 2024-2034 - Changlin Wan, Pengtao Dang, Tong Zhao, Yong Zang, Chi Zhang, Sha Cao:
Bias aware probabilistic Boolean matrix factorization. 2035-2044 - Zhenyi Wang, Xiaoyang Wang, Li Shen, Qiuling Suo, Kaiqiang Song, Dong Yu, Yan Shen, Mingchen Gao:
Meta-learning without data via Wasserstein distributionally-robust model fusion. 2045-2055 - Xiaosen Wang, Yifeng Xiong, Kun He:
Detecting textual adversarial examples through randomized substitution and vote. 2056-2065 - Zhe Wang, Jake Grigsby, Arshdeep Sekhon, Yanjun Qi:
ST-MAML : A stochastic-task based method for task-heterogeneous meta-learning. 2066-2074 - Haohan Wang, Zeyi Huang, Hanlin Zhang, Yong Jae Lee, Eric P. Xing:
Toward learning human-aligned cross-domain robust models by countering misaligned features. 2075-2084 - Houston Warren, Rafael Oliveira, Fabio T. Ramos:
Generalized Bayesian quadrature with spectral kernels. 2085-2095 - David S. Watson, Ricardo Silva:
Causal discovery under a confounder blanket. 2096-2106 - Marcel Wienöbst, Max Bannach, Maciej Liskiewicz:
A new constructive criterion for Markov equivalence of MAGs. 2107-2116 - Shuang Wu, Chi-Hua Wang, Yuantong Li, Guang Cheng:
Residual bootstrap exploration for stochastic linear bandit. 2117-2127 - Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q. Weinberger, Chong Wang:
Differentially private multi-party data release for linear regression. 2128-2137 - Han Wu, Stefan Wager:
Partial likelihood Thompson sampling. 2138-2147 - Xiumin Xie, Chuanwen Hou, Zhixin Li:
Fine-Grained matching with multi-perspective similarity modeling for cross-modal retrieval. 2148-2158 - Huaqing Xiong, Tengyu Xu, Lin Zhao, Yingbin Liang, Wei Zhang:
Deterministic policy gradient: Convergence analysis. 2159-2169 - Rui Yan, Gabriel Santos, Xiaoming Duan, David Parker, Marta Kwiatkowska:
Finite-horizon equilibria for neuro-symbolic concurrent stochastic games. 2170-2180 - Hanqi Yan, Lin Gui, Wenjie Li, Yulan He:
Addressing token uniformity in transformers via singular value transformation. 2181-2191 - Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying:
Differentially private SGDA for minimax problems. 2192-2202 - Huanhuan Yang, Dianxi Shi, Guojun Xie, Yingxuan Peng, Yi Zhang, Yantai Yang, Shaowu Yang:
Self-supervised representations for multi-view reinforcement learning. 2203-2213 - Yichen Yang, Xiaosen Wang, Kun He:
Robust textual embedding against word-level adversarial attacks. 2214-2224 - Shoujian Yang, Lian Yu:
CoSPA: An improved masked language model with copy mechanism for Chinese spelling correction. 2225-2234 - Yingzhen Yang, Ping Li:
Noisy L0-sparse subspace clustering on dimensionality reduced data. 2235-2245 - Mao Ye, Qiang Liu:
Pareto navigation gradient descent: a first-order algorithm for optimization in pareto set. 2246-2255 - Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu:
Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems. 2256-2266 - Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy:
Superposing many tickets into one: A performance booster for sparse neural network training. 2267-2277 - Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang:
Offline stochastic shortest path: Learning, evaluation and towards optimality. 2278-2288 - Gal Yona, Shay Moran, Gal Elidan, Amir Globerson:
Active learning with label comparisons. 2289-2298 - Heng You, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor:
Cross-domain adaptive transfer reinforcement learning based on state-action correspondence. 2299-2309 - Sixie Yu, P. Jeffrey Brantingham, Matthew Valasik, Yevgeniy Vorobeychik:
Learning binary multi-scale games on networks. 2310-2319 - Zhongjie Yu, Fabrizio Ventola, Nils Thoma, Devendra Singh Dhami, Martin Mundt, Kristian Kersting:
Predictive Whittle networks for time series. 2320-2330 - Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, José C. Príncipe:
Principle of relevant information for graph sparsification. 2331-2341 - Xueying Zhan, Yaowei Wang, Antoni B. Chan:
Asymptotic optimality for active learning processes. 2342-2352 - Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu:
Distributed adversarial training to robustify deep neural networks at scale. 2353-2363 - Yikai Zhang, Wenjia Zhang, Sammy Bald, Vamsi Pingali, Chao Chen, Mayank Goswami:
Stability of SGD: Tightness analysis and improved bounds. 2364-2373 - Zeyu Zhao, Ke Xu, Xinghao Jiang, Tanfeng Sun:
Research on video adversarial attack with long living cycle. 2374-2382 - Fangting Zhou, Kejun He, Yang Ni:
Causal discovery with heterogeneous observational data. 2383-2393 - Qiang Zhou, Sinno Jialin Pan:
Convergence Analysis of Linear Coupling with Inexact Proximal Operator. 2394-2403 - Chenghan Zhou, Andrew Spivey, Haifeng Xu, Thanh Hong Nguyen:
Information design for multiple independent and self-interested defenders: Work less, pay off more. 2404-2413 - Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva:
Causal inference with treatment measurement error: a nonparametric instrumental variable approach. 2414-2424
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