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ORCID37th UAI 2021: Virtual Event
- Cassio P. de Campos, Marloes H. Maathuis, Erik Quaeghebeur:
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Virtual Event, 27-30 July 2021. Proceedings of Machine Learning Research 161, AUAI Press 2021 - Preface and Frontmatter. 1-11
- Thomas Ryder, Dennis Prangle, Andrew Golightly, Isaac Matthews:
The neural moving average model for scalable variational inference of state space models. 12-22 - Pan Zhou, Yingtian Zou, Xiao-Tong Yuan, Jiashi Feng, Caiming Xiong, Steven C. H. Hoi:
Task similarity aware meta learning: theory-inspired improvement on MAML. 23-33 - Kei Ishikawa, Takashi Goda:
Efficient debiased evidence estimation by multilevel Monte Carlo sampling. 34-43 - Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational inference with continuously-indexed normalizing flows. 44-53 - Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, Lei Lyu:
TreeBERT: A tree-based pre-trained model for programming language. 54-63 - Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar:
Competitive policy optimization. 64-74 - Chunwei Ma, Ziyun Huang
, Jiayi Xian, Mingchen Gao, Jinhui Xu:
Improving uncertainty calibration of deep neural networks via truth discovery and geometric optimization. 75-85 - Takeshi Teshima, Masashi Sugiyama:
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. 86-96 - Takashi Nicholas Maeda, Shohei Shimizu:
Causal additive models with unobserved variables. 97-106 - Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh:
A variational approximation for analyzing the dynamics of panel data. 107-117 - Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande, Vikas Singh:
Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks. 118-128 - Yifei Min, Lin Chen, Amin Karbasi:
The curious case of adversarially robust models: More data can help, double descend, or hurt generalization. 129-139 - Yizhao Gao, Nanyi Fei, Guangzhen Liu, Zhiwu Lu, Tao Xiang:
Contrastive prototype learning with augmented embeddings for few-shot learning. 140-150 - Fan Ding, Yexiang Xue:
XOR-SGD: provable convex stochastic optimization for decision-making under uncertainty. 151-160 - Ranjani Srinivasan, Jaron J. R. Lee, Rohit Bhattacharya, Ilya Shpitser:
Path dependent structural equation models. 161-171 - Kristy Choi, Madeline Liao, Stefano Ermon:
Featurized density ratio estimation. 172-182 - Rameshwar Pratap, Raghav Kulkarni:
Variance reduction in frequency estimators via control variates method. 183-193 - Alexis Bellot, Mihaela van der Schaar:
Application of kernel hypothesis testing on set-valued data. 194-204 - Alexis Bellot, Mihaela van der Schaar:
A kernel two-sample test with selection bias. 205-214 - Chiu-Chou Lin, Wei-Chen Chiu, I-Chen Wu:
An unsupervised video game playstyle metric via state discretization. 215-224 - Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung:
Most: multi-source domain adaptation via optimal transport for student-teacher learning. 225-235 - Chidubem Arachie, Bert Huang:
Constrained labeling for weakly supervised learning. 236-246 - Mridul Agarwal, Bhargav Ganguly, Vaneet Aggarwal:
Communication efficient parallel reinforcement learning. 247-256 - Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe:
Robust reinforcement learning under minimax regret for green security. 257-267 - Hu Ding, Fan Yang, Jiawei Huang:
Defending SVMs against poisoning attacks: the hardness and DBSCAN approach. 268-278 - Brendan O'Donoghue, Tor Lattimore, Ian Osband:
Matrix games with bandit feedback. 279-289 - Gaspard Beugnot, Aude Genevay, Kristjan H. Greenewald, Justin Solomon:
Improving approximate optimal transport distances using quantization. 290-300 - Batya Kenig:
Approximate implication with d-separation. 301-311 - Simon Luo, Lamiae Azizi, Mahito Sugiyama:
Hierarchical probabilistic model for blind source separation via Legendre transformation. 312-321 - Jonathan Feldstein, Vaishak Belle:
Lifted reasoning meets weighted model integration. 322-332 - Davide Corsi, Enrico Marchesini, Alessandro Farinelli:
Formal verification of neural networks for safety-critical tasks in deep reinforcement learning. 333-343 - Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Learnable uncertainty under Laplace approximations. 344-353 - Sun Sun, Hongyu Guo:
Symmetric Wasserstein autoencoders. 354-364 - Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi:
Unsupervised anomaly detection with adversarial mirrored autoencoders. 365-375 - Nir Baram, Guy Tennenholtz, Shie Mannor:
Action redundancy in reinforcement learning. 376-385 - Paulius Dilkas, Vaishak Belle:
Weighted model counting with conditional weights for Bayesian networks. 386-396 - Jyun-Li Lin, Wei Hung, Shang-Hsuan Yang, Ping-Chun Hsieh, Xi Liu:
Escaping from zero gradient: Revisiting action-constrained reinforcement learning via Frank-Wolfe policy optimization. 397-407 - Chenghui Zhou, Chun-Liang Li, Barnabás Póczos:
Unsupervised program synthesis for images by sampling without replacement. 408-418 - Zhiyi Zhang, Ziyin Liu:
On the distributional properties of adaptive gradients. 419-429 - Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni:
Bandits with partially observable confounded data. 430-439 - Xinyu Zhang, Kai Zhao, Taihong Xiao, Ming-Ming Cheng, Ming-Hsuan Yang:
Structured sparsification with joint optimization of group convolution and channel shuffle. 440-450 - Alexander Marx, Arthur Gretton, Joris M. Mooij:
A weaker faithfulness assumption based on triple interactions. 451-460 - Michael Kirchhof, Lena Schmid, Christopher Reining, Michael ten Hompel, Markus Pauly:
pRSL: Interpretable multi-label stacking by learning probabilistic rules. 461-470 - David Klaska, Antonín Kucera, Vít Musil, Vojtech Rehák:
Regstar: efficient strategy synthesis for adversarial patrolling games. 471-481 - Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He:
The complexity of nonconvex-strongly-concave minimax optimization. 482-492 - David Eriksson, Martin Jankowiak:
High-dimensional Bayesian optimization with sparse axis-aligned subspaces. 493-503 - Harsh Agrawal, Eli A. Meirom, Yuval Atzmon, Shie Mannor, Gal Chechik:
Known unknowns: Learning novel concepts using reasoning-by-elimination. 504-514 - Bingyuan Zhang, Jie Chen, Yoshikazu Terada:
Dynamic visualization for L1 fusion convex clustering in near-linear time. 515-524 - Arnab Kumar Mondal, Himanshu Asnani, Parag Singla, A. P. Prathosh:
FlexAE: flexibly learning latent priors for wasserstein auto-encoders. 525-535 - Kshitij Gajjar, Girish Varma, Prerona Chatterjee, Jaikumar Radhakrishnan:
Generalized parametric path problems. 536-546 - Huang Fang, Guanhua Fang, Tan Yu, Ping Li:
Efficient greedy coordinate descent via variable partitioning. 547-557 - Shikai Fang, Robert M. Kirby, Shandian Zhe:
Bayesian streaming sparse Tucker decomposition. 558-567 - Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger:
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. 568-577 - George De Ath, Richard M. Everson, Jonathan E. Fieldsend:
Asynchronous ε-Greedy Bayesian Optimisation. 578-588 - Géraldin Nanfack, Paul Temple, Benoît Frénay:
Global explanations with decision rules: a co-learning approach. 589-599 - Kirtan Padh, Diego Antognini, Emma Lejal Glaude, Boi Faltings, Claudiu Musat:
Addressing fairness in classification with a model-agnostic multi-objective algorithm. 600-609 - Shuwa Miura, Shlomo Zilberstein:
A unifying framework for observer-aware planning and its complexity. 610-620 - Komal Dhull, Jingyan Wang, Nihar B. Shah, Yuanzhi Li, R. Ravi:
A heuristic for statistical seriation. 621-631 - Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing communication rounds for distributed learning. 632-642 - Yuting Ng, Ali Hasan, Khalil Elkhalil, Vahid Tarokh:
Generative Archimedean copulas. 643-653 - Colin White, Sam Nolen, Yash Savani:
Exploring the loss landscape in neural architecture search. 654-664 - Bowen Weng, Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang:
Finite-time theory for momentum Q-learning. 665-674 - Adam D. Cobb, Brian Jalaian:
Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting. 675-685 - Frank Nussbaum, Joachim Giesen:
Robust principal component analysis for generalized multi-view models. 686-695 - Ramina Ghods, Arundhati Banerjee, Jeff Schneider:
Decentralized multi-agent active search for sparse signals. 696-706 - Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet:
Unbiased gradient estimation for variational auto-encoders using coupled Markov chains. 707-717 - Loïc Adam, Sébastien Destercke:
Possibilistic preference elicitation by minimax regret. 718-727 - Simon S. Du, Wei Hu, Zhiyuan Li, Ruoqi Shen, Zhao Song, Jiajun Wu:
When is particle filtering efficient for planning in partially observed linear dynamical systems? 728-737 - Anthony DiGiovanni, Ambuj Tewari:
Thompson sampling for Markov games with piecewise stationary opponent policies. 738-748 - Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen J. Roberts:
Hierarchical Indian buffet neural networks for Bayesian continual learning. 749-759 - Awni Y. Hannun, Chuan Guo, Laurens van der Maaten:
Measuring data leakage in machine-learning models with Fisher information. 760-770 - Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano:
Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces. 771-780 - Cuong Cao Nguyen, Thanh-Toan Do, Gustavo Carneiro:
Probabilistic task modelling for meta-learning. 781-791 - Naoto Ohsaka, Tatsuya Matsuoka:
Approximation algorithm for submodular maximization under submodular cover. 792-801 - Fengxiang He, Bohan Wang, Dacheng Tao:
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms. 802-812 - Robin J. Evans:
Dependency in DAG models with hidden variables. 813-822 - Xiaosen Wang, Jin Hao, Yichen Yang, Kun He:
Natural language adversarial defense through synonym encoding. 823-833 - Xufang Luo, Qi Meng, Wei Chen, Yunhong Wang, Tie-Yan Liu:
Path-BN: Towards effective batch normalization in the Path Space for ReLU networks. 834-843 - Aleksandr Podkopaev, Aaditya Ramdas:
Distribution-free uncertainty quantification for classification under label shift. 844-853 - Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Björn M. Eskofier:
Identifying untrustworthy predictions in neural networks by geometric gradient analysis. 854-864 - Wei Chen, Liwei Wang, Haoyu Zhao, Kai Zheng:
Combinatorial semi-bandit in the non-stationary environment. 865-875 - Giuseppe Canonaco, Andrea Soprani, Matteo Giuliani, Andrea Castelletti, Manuel Roveri, Marcello Restelli:
Time-variant variational transfer for value functions. 876-886 - Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, David Flynn:
BayLIME: Bayesian local interpretable model-agnostic explanations. 887-896 - Etai Littwin, Tomer Galanti, Lior Wolf:
On random kernels of residual architectures. 897-907 - Giuseppe Marra, Ondrej Kuzelka:
Neural markov logic networks. 908-917 - Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han:
Deep kernels with probabilistic embeddings for small-data learning. 918-928 - Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues:
On the effects of quantisation on model uncertainty in Bayesian neural networks. 929-938 - Jens Petersen, Gregor Köhler, David Zimmerer, Fabian Isensee, Paul F. Jäger, Klaus H. Maier-Hein:
GP-ConvCNP: Better generalization for conditional convolutional Neural Processes on time series data. 939-949 - ChangYong Oh, Efstratios Gavves, Max Welling:
Mixed variable Bayesian optimization with frequency modulated kernels. 950-960 - Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar:
Subseasonal climate prediction in the western US using Bayesian spatial models. 961-970 - Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David M. Blei, Itsik Pe'er:
variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference. 971-981 - Shantanu Gupta, Zachary C. Lipton, David Childers:
Estimating treatment effects with observed confounders and mediators. 982-991 - Mahsa Ghasemi, Abolfazl Hashemi, Haris Vikalo, Ufuk Topcu:
No-regret learning with high-probability in adversarial Markov decision processes. 992-1001 - Sihan Zeng, Malik Aqeel Anwar, Thinh T. Doan, Arijit Raychowdhury, Justin Romberg:
A decentralized policy gradient approach to multi-task reinforcement learning. 1002-1012 - Eigil Fjeldgren Rischel, Sebastian Weichwald:
Compositional abstraction error and a category of causal models. 1013-1023 - Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer:
Bayesian optimization for modular black-box systems with switching costs. 1024-1034 - Anders Kirk Uhrenholt, Valentin Charvet, Bjørn Sand Jensen:
Probabilistic selection of inducing points in sparse Gaussian processes. 1035-1044 - Noam Finkelstein, Beata Zjawin, Elie Wolfe, Ilya Shpitser, Robert W. Spekkens:
Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables. 1045-1055 - Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem van de Meent:
Learning proposals for probabilistic programs with inference combinators. 1056-1066 - Feras A. Saad, Vikash K. Mansinghka:
Hierarchical infinite relational model. 1067-1077 - Sambaran Bandyopadhyay, Vishal Peter:
Unsupervised constrained community detection via self-expressive graph neural network. 1078-1088 - Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao:
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation. 1089-1099 - Fan Ding, Nan Jiang, Jianzhu Ma, Jian Peng, Jinbo Xu, Yexiang Xue:
PALM: Probabilistic area loss Minimization for Protein Sequence Alignment. 1100-1109 - Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu:
Principal component analysis in the stochastic differential privacy model. 1110-1119 - Pinyan Lu, Chao Tao, Xiaojin Zhang:
Variance-dependent best arm identification. 1120-1129 - Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Stochastic continuous normalizing flows: training SDEs as ODEs. 1130-1140 - Minkyo Seo, Yoonho Lee, Suha Kwak:
On the distribution of penultimate activations of classification networks. 1141-1151 - Difan Zou, Pan Xu, Quanquan Gu:
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. 1152-1162 - Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck:
Tractable computation of expected kernels. 1163-1173 - Nir Ailon, Omer Leibovitch, Vineet Nair:
Sparse linear networks with a fixed butterfly structure: theory and practice. 1174-1184 - Topi Paananen, Michael Riis Andersen, Aki Vehtari:
Uncertainty-aware sensitivity analysis using Rényi divergences. 1185-1194 - Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier:
Testification of Condorcet Winners in dueling bandits. 1195-1205 - Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk:
The promises and pitfalls of deep kernel learning. 1206-1216 - David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton:
Confidence in causal discovery with linear causal models. 1217-1226 - Nicolas Schreuder, Evgenii Chzhen:
Classification with abstention but without disparities. 1227-1236 - Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Maximal ancestral graph structure learning via exact search. 1237-1247 - Marcel Wienöbst, Max Bannach, Maciej Liskiewicz:
Extendability of causal graphical models: Algorithms and computational complexity. 1248-1257 - Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra, Xenia Miscouridou, Ricardo P. Schnekenberg, Charles Whittaker, Michaela A. C. Vollmer, Seth R. Flaxman, Samir Bhatt, Thomas A. Mellan:
Gaussian process nowcasting: application to COVID-19 mortality reporting. 1258-1268 - Konik Kothari, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanic:
Trumpets: Injective flows for inference and inverse problems. 1269-1278 - Jon M. Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar:
Stochastic model for sunk cost bias. 1279-1288 - Nicola Branchini, Víctor Elvira:
Optimized auxiliary particle filters: adapting mixture proposals via convex optimization. 1289-1299 - Ludvig Hult, Dave Zachariah:
Inference of causal effects when control variables are unknown. 1300-1309 - Yurong Ling, Zijing Liu, Jing-Hao Xue:
Dimension reduction for data with heterogeneous missingness. 1310-1320 - Georgii S. Novikov, Maxim E. Panov, Ivan V. Oseledets:
Tensor-train density estimation. 1321-1331 - Zijing Liu, Mauricio Barahona:
Similarity measure for sparse time course data based on Gaussian processes. 1332-1341 - Beyza Ermis, Giovanni Zappella, Cédric Archambeau:
Towards robust episodic meta-learning. 1342-1351 - Thomas Bachlechner, Bodhisattwa Prasad Majumder, Huanru Henry Mao, Gary Cottrell, Julian J. McAuley:
ReZero is all you need: fast convergence at large depth. 1352-1361