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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 - Xin-Yu 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 - Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan:
Subset-of-data variational inference for deep Gaussian-processes regression. 1362-1370 - Andrew H. Song, Demba E. Ba, Emery N. Brown:
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components. 1371-1381 - David S. Watson, Limor Gultchin, Ankur Taly, Luciano Floridi:
Local explanations via necessity and sufficiency: unifying theory and practice. 1382-1392 - Timothy van Bremen, Ondrej Kuzelka:
Faster lifting for two-variable logic using cell graphs. 1393-1402 - Meet P. Vadera, Soumya Ghosh, Kenney Ng, Benjamin M. Marlin:
Post-hoc loss-calibration for Bayesian neural networks. 1403-1412 - Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts:
Towards tractable optimism in model-based reinforcement learning. 1413-1423 - Julia Grosse, Cheng Zhang, Philipp Hennig:
Probabilistic DAG search. 1424-1433 - Sofia Triantafillou, Fattaneh Jabbari, Gregory F. Cooper:
Causal and interventional Markov boundaries. 1434-1443 - Haike Xu, Jian Li:
Simple combinatorial algorithms for combinatorial bandits: corruptions and approximations. 1444-1454 - Dexun Li, Meghna Lowalekar, Pradeep Varakantham:
CLAIM: curriculum learning policy for influence maximization in unknown social networks. 1455-1465 - Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Learning to learn with Gaussian processes. 1466-1475 - Jasper De Bock, Alexander Erreygers, Thomas Krak:
Sum-product laws and efficient algorithms for imprecise Markov chains. 1476-1485 - Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet:
Trusted-maximizers entropy search for efficient Bayesian optimization. 1486-1495 - Qiwen Cui, Lin F. Yang:
Minimax sample complexity for turn-based stochastic game. 1496-1504 - Yinchong Yang, Florian Buettner:
Multi-output Gaussian Processes for uncertainty-aware recommender systems. 1505-1514 - Boris Joukovsky, Tanmoy Mukherjee, Huynh Van Luong, Nikos Deligiannis:
Generalization error bounds for deep unfolding RNNs. 1515-1524 - Bhavya Kalra, Kulin Shah, Naresh Manwani:
RISAN: Robust instance specific deep abstention network. 1525-1534 - Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, Milind Tambe:
Contingency-aware influence maximization: A reinforcement learning approach. 1535-1545 - Claudia Shi, Victor Veitch, David M. Blei:
Invariant representation learning for treatment effect estimation. 1546-1555 - Florian Jaeckle, M. Pawan Kumar:
Generating adversarial examples with graph neural networks. 1556-1564 - Philip A. Boeken, Joris M. Mooij:
A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery. 1565-1575 - Shreyas Sheshadri, Avirup Saha, Priyank Patel, Samik Datta, Niloy Ganguly:
Graph-based semi-supervised learning through the lens of safety. 1576-1586 - Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann:
Strategically efficient exploration in competitive multi-agent reinforcement learning. 1587-1596 - Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov:
Information theoretic meta learning with Gaussian processes. 1597-1606 - Will Tebbutt, Arno Solin, Richard E. Turner:
Combining pseudo-point and state space approximations for sum-separable Gaussian Processes. 1607-1617 - Harsh Rangwani, Konda Reddy Mopuri, R. Venkatesh Babu:
Class balancing GAN with a classifier in the loop. 1618-1627 - Hui Lan, Antoni B. Chan:
Hierarchical learning of Hidden Markov Models with clustering regularization. 1628-1638 - Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang:
Enabling long-range exploration in minimization of multimodal functions. 1639-1649 - Alon Brutzkus, Amir Globerson:
An optimization and generalization analysis for max-pooling networks. 1650-1660 - Ezgi Korkmaz:
Investigating vulnerabilities of deep neural policies. 1661-1670 - Ramit Sawhney, Arnav Wadhwa, Ayush Mangal, Vivek Mittal, Shivam Agarwal, Rajiv Ratn Shah:
Modeling financial uncertainty with multivariate temporal entropy-based curriculums. 1671-1681 - Nicola Di Mauro, Gennaro Gala, Marco Iannotta, Teresa M. A. Basile:
Random probabilistic circuits. 1682-1691 - Leonardo Cella, Massimiliano Pontil:
Multi-task and meta-learning with sparse linear bandits. 1692-1702 - Khaoula el Mekkaoui, Diego Mesquita, Paul Blomstedt, Samuel Kaski:
Federated stochastic gradient Langevin dynamics. 1703-1712 - Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska:
Certification of iterative predictions in Bayesian neural networks. 1713-1723 - Samarth Gupta, Saurabh Amin:
Integer programming-based error-correcting output code design for robust classification. 1724-1734 - Benjie Wang, Stefan Webb, Tom Rainforth:
Statistically robust neural network classification. 1735-1745 - Carlos Améndola, Benjamin Hollering, Seth Sullivant, Ngoc Tran:
Markov equivalence of max-linear Bayesian networks. 1746-1755 - Raouf Kerkouche, Gergely Ács, Claude Castelluccia, Pierre Genevès:
Constrained differentially private federated learning for low-bandwidth devices. 1756-1765 - Dennis Ulmer, Giovanni Cinà:
Know your limits: Uncertainty estimation with ReLU classifiers fails at reliable OOD detection. 1766-1776 - Blake Mason, Ardhendu Tripathy, Robert Nowak:
Nearest neighbor search under uncertainty. 1777-1786 - Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts. 1787-1797 - Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser:
Partial Identifiability in Discrete Data with Measurement Error. 1798-1808 - Dylan Troop, Frédéric Godin, Jia Yuan Yu:
Bias-corrected peaks-over-threshold estimation of the CVaR. 1809-1818 - Ghassen Jerfel, Serena Lutong Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan:
Variational refinement for importance sampling using the forward Kullback-Leibler divergence. 1819-1829 - David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee:
Diagnostics for conditional density models and Bayesian inference algorithms. 1830-1840 - Zhili Feng, Fred Roosta, David P. Woodruff:
Non-PSD matrix sketching with applications to regression and optimization. 1841-1851 - Jeffrey Gu, Serena Yeung:
Staying in shape: learning invariant shape representations using contrastive learning. 1852-1862 - Harald Leisenberger, Christian Knoll, Richard Seeber, Franz Pernkopf:
Convergence behavior of belief propagation: estimating regions of attraction via Lyapunov functions. 1863-1873 - Xinyan Yan, Byron Boots, Ching-An Cheng:
Explaining fast improvement in online imitation learning. 1874-1884 - Nitin Kamra, Yan Liu:
Gradient-based optimization for multi-resource spatial coverage problems. 1885-1894 - Aaron Schein, Anjali Nagulpally, Hanna M. Wallach, Patrick Flaherty:
Doubly non-central beta matrix factorization for DNA methylation data. 1895-1904 - Shiva Prasad Kasiviswanathan:
SGD with low-dimensional gradients with applications to private and distributed learning. 1905-1915 - Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams:
Active multi-fidelity Bayesian online changepoint detection. 1916-1926 - William Brown:
Learning in Multi-Player Stochastic Games. 1927-1937 - Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood:
q-Paths: Generalizing the geometric annealing path using power means. 1938-1947 - Spencer L. Gordon, Vinayak M. Kumar, Leonard J. Schulman, Piyush Srivastava:
Condition number bounds for causal inference. 1948-1957 - Apoorva Sharma, Navid Azizan, Marco Pavone:
Sketching curvature for efficient out-of-distribution detection for deep neural networks. 1958-1967 - Nan Wang, Branislav Kveton, Maryam Karimzadehgan:
CORe: Capitalizing On Rewards in Bandit Exploration. 1968-1978 - Yu Wang, Yuesong Shen, Daniel Cremers:
Explicit pairwise factorized graph neural network for semi-supervised node classification. 1979-1987 - Rupam Acharyya, Ankani Chattoraj, Boyu Zhang, Shouman Das, Daniel Stefankovic:
Statistical mechanical analysis of neural network pruning. 1988-1997 - Adrià Garriga-Alonso, Mark van der Wilk:
Correlated weights in infinite limits of deep convolutional neural networks. 1998-2007 - Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting:
Leveraging probabilistic circuits for nonparametric multi-output regression. 2008-2018 - Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal, Andreas M. Lehrmann:
PROVIDE: a probabilistic framework for unsupervised video decomposition. 2019-2028 - Amirhossein Kardoost, Margret Keuper:
Uncertainty in minimum cost multicuts for image and motion segmentation. 2029-2038 - Renato Lui Geh, Denis Deratani Mauá:
Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging. 2039-2049 - Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu:
Conditionally independent data generation. 2050-2060 - Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum:
Exact and approximate hierarchical clustering using A. 2061-2071 - Rylan Schaeffer, Blake Bordelon, Mikail Khona, Weiwei Pan, Ila Rani Fiete:
Efficient online inference for nonparametric mixture models. 2072-2081 - Rafael Oliveira, Lionel Ott, Fabio Ramos:
No-regret approximate inference via Bayesian optimisation. 2082-2092 - Abhinav Kumar, Gaurav Sinha:
Disentangling mixtures of unknown causal interventions. 2093-2102 - Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava:
SDM-Net: A simple and effective model for generalized zero-shot learning. 2103-2113 - Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik:
Towards a unified framework for fair and stable graph representation learning. 2114-2124 - Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner:
Identifying regions of trusted predictions. 2125-2134 - Ji Gao, Amin Karbasi, Mohammad Mahmoody:
Learning and certification under instance-targeted poisoning. 2135-2145 - Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, Andrew McCallum:
Min/max stability and box distributions. 2146-2155 - Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney:
Geometric rates of convergence for kernel-based sampling algorithms. 2156-2164 - Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell:
Sequential core-set Monte Carlo. 2165-2175
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