| 2021 | A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery. Philip A. Boeken, Joris M. Mooij |
| 2021 | A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger |
| 2021 | A decentralized policy gradient approach to multi-task reinforcement learning. Sihan Zeng, Malik Aqeel Anwar, Thinh T. Doan, Arijit Raychowdhury, Justin Romberg |
| 2021 | A heuristic for statistical seriation. Komal Dhull, Jingyan Wang, Nihar B. Shah, Yuanzhi Li, R. Ravi |
| 2021 | A kernel two-sample test with selection bias. Alexis Bellot, Mihaela van der Schaar |
| 2021 | A unifying framework for observer-aware planning and its complexity. Shuwa Miura, Shlomo Zilberstein |
| 2021 | A variational approximation for analyzing the dynamics of panel data. Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh |
| 2021 | A weaker faithfulness assumption based on triple interactions. Alexander Marx, Arthur Gretton, Joris M. Mooij |
| 2021 | Action redundancy in reinforcement learning. Nir Baram, Guy Tennenholtz, Shie Mannor |
| 2021 | Active multi-fidelity Bayesian online changepoint detection. Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams |
| 2021 | Addressing fairness in classification with a model-agnostic multi-objective algorithm. Kirtan Padh, Diego Antognini, Emma Lejal Glaude, Boi Faltings, Claudiu Musat |
| 2021 | An optimization and generalization analysis for max-pooling networks. Alon Brutzkus, Amir Globerson |
| 2021 | An unsupervised video game playstyle metric via state discretization. Chiu-Chou Lin, Wei-Chen Chiu, I-Chen Wu |
| 2021 | Application of kernel hypothesis testing on set-valued data. Alexis Bellot, Mihaela van der Schaar |
| 2021 | Approximate implication with d-separation. Batya Kenig |
| 2021 | Approximation algorithm for submodular maximization under submodular cover. Naoto Ohsaka, Tatsuya Matsuoka |
| 2021 | Asynchronous ε-Greedy Bayesian Optimisation. George De Ath, Richard M. Everson, Jonathan E. Fieldsend |
| 2021 | Bandits with partially observable confounded data. Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni |
| 2021 | BayLIME: Bayesian local interpretable model-agnostic explanations. Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, David Flynn |
| 2021 | Bayesian optimization for modular black-box systems with switching costs. Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer |
| 2021 | Bayesian streaming sparse Tucker decomposition. Shikai Fang, Robert M. Kirby, Shandian Zhe |
| 2021 | Bias-corrected peaks-over-threshold estimation of the CVaR. Dylan Troop, Frédéric Godin, Jia Yuan Yu |
| 2021 | CLAIM: curriculum learning policy for influence maximization in unknown social networks. Dexun Li, Meghna Lowalekar, Pradeep Varakantham |
| 2021 | CORe: Capitalizing On Rewards in Bandit Exploration. Nan Wang, Branislav Kveton, Maryam Karimzadehgan |
| 2021 | Causal additive models with unobserved variables. Takashi Nicholas Maeda, Shohei Shimizu |
| 2021 | Causal and interventional Markov boundaries. Sofia Triantafillou, Fattaneh Jabbari, Gregory F. Cooper |
| 2021 | Certification of iterative predictions in Bayesian neural networks. Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska |
| 2021 | Class balancing GAN with a classifier in the loop. Harsh Rangwani, Konda Reddy Mopuri, R. Venkatesh Babu |
| 2021 | Classification with abstention but without disparities. Nicolas Schreuder, Evgenii Chzhen |
| 2021 | Combinatorial semi-bandit in the non-stationary environment. Wei Chen, Liwei Wang, Haoyu Zhao, Kai Zheng |
| 2021 | Combining pseudo-point and state space approximations for sum-separable Gaussian Processes. Will Tebbutt, Arno Solin, Richard E. Turner |
| 2021 | Communication efficient parallel reinforcement learning. Mridul Agarwal, Bhargav Ganguly, Vaneet Aggarwal |
| 2021 | Competitive policy optimization. Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar |
| 2021 | Compositional abstraction error and a category of causal models. Eigil Fjeldgren Rischel, Sebastian Weichwald |
| 2021 | Condition number bounds for causal inference. Spencer L. Gordon, Vinayak M. Kumar, Leonard J. Schulman, Piyush Srivastava |
| 2021 | Conditionally independent data generation. Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu |
| 2021 | Confidence in causal discovery with linear causal models. David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton |
| 2021 | Constrained differentially private federated learning for low-bandwidth devices. Raouf Kerkouche, Gergely Ács, Claude Castelluccia, Pierre Genevès |
| 2021 | Constrained labeling for weakly supervised learning. Chidubem Arachie, Bert Huang |
| 2021 | Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts. Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee |
| 2021 | Contingency-aware influence maximization: A reinforcement learning approach. Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, Milind Tambe |
| 2021 | Contrastive prototype learning with augmented embeddings for few-shot learning. Yizhao Gao, Nanyi Fei, Guangzhen Liu, Zhiwu Lu, Tao Xiang |
| 2021 | Convergence behavior of belief propagation: estimating regions of attraction via Lyapunov functions. Harald Leisenberger, Christian Knoll, Richard Seeber, Franz Pernkopf |
| 2021 | Correlated weights in infinite limits of deep convolutional neural networks. Adrià Garriga-Alonso, Mark van der Wilk |
| 2021 | Decentralized multi-agent active search for sparse signals. Ramina Ghods, Arundhati Banerjee, Jeff Schneider |
| 2021 | Deep kernels with probabilistic embeddings for small-data learning. Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han |
| 2021 | Defending SVMs against poisoning attacks: the hardness and DBSCAN approach. Hu Ding, Fan Yang, Jiawei Huang |
| 2021 | Dependency in DAG models with hidden variables. Robin J. Evans |
| 2021 | Diagnostics for conditional density models and Bayesian inference algorithms. David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee |
| 2021 | Dimension reduction for data with heterogeneous missingness. Yurong Ling, Zijing Liu, Jing-Hao Xue |
| 2021 | Disentangling mixtures of unknown causal interventions. Abhinav Kumar, Gaurav Sinha |
| 2021 | Distribution-free uncertainty quantification for classification under label shift. Aleksandr Podkopaev, Aaditya Ramdas |
| 2021 | Doubly non-central beta matrix factorization for DNA methylation data. Aaron Schein, Anjali Nagulpally, Hanna M. Wallach, Patrick Flaherty |
| 2021 | Dynamic visualization for L1 fusion convex clustering in near-linear time. Bingyuan Zhang, Jie Chen, Yoshikazu Terada |
| 2021 | Efficient debiased evidence estimation by multilevel Monte Carlo sampling. Kei Ishikawa, Takashi Goda |
| 2021 | Efficient greedy coordinate descent via variable partitioning. Huang Fang, Guanhua Fang, Tan Yu, Ping Li |
| 2021 | Efficient online inference for nonparametric mixture models. Rylan Schaeffer, Blake Bordelon, Mikail Khona, Weiwei Pan, Ila Rani Fiete |
| 2021 | Enabling long-range exploration in minimization of multimodal functions. Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang |
| 2021 | Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables. Noam Finkelstein, Beata Zjawin, Elie Wolfe, Ilya Shpitser, Robert W. Spekkens |
| 2021 | Escaping from zero gradient: Revisiting action-constrained reinforcement learning via Frank-Wolfe policy optimization. Jyun-Li Lin, Wei Hung, Shang-Hsuan Yang, Ping-Chun Hsieh, Xi Liu |
| 2021 | Estimating treatment effects with observed confounders and mediators. Shantanu Gupta, Zachary C. Lipton, David Childers |
| 2021 | Exact and approximate hierarchical clustering using A. Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum |
| 2021 | Explaining fast improvement in online imitation learning. Xinyan Yan, Byron Boots, Ching-An Cheng |
| 2021 | Explicit pairwise factorized graph neural network for semi-supervised node classification. Yu Wang, Yuesong Shen, Daniel Cremers |
| 2021 | Exploring the loss landscape in neural architecture search. Colin White, Sam Nolen, Yash Savani |
| 2021 | Extendability of causal graphical models: Algorithms and computational complexity. Marcel Wienöbst, Max Bannach, Maciej Liskiewicz |
| 2021 | Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. Difan Zou, Pan Xu, Quanquan Gu |
| 2021 | Faster lifting for two-variable logic using cell graphs. Timothy van Bremen, Ondrej Kuzelka |
| 2021 | Featurized density ratio estimation. Kristy Choi, Madeline Liao, Stefano Ermon |
| 2021 | Federated stochastic gradient Langevin dynamics. Khaoula el Mekkaoui, Diego Mesquita, Paul Blomstedt, Samuel Kaski |
| 2021 | Finite-time theory for momentum Q-learning. Bowen Weng, Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang |
| 2021 | FlexAE: flexibly learning latent priors for wasserstein auto-encoders. Arnab Kumar Mondal, Himanshu Asnani, Parag Singla, A. P. Prathosh |
| 2021 | Formal verification of neural networks for safety-critical tasks in deep reinforcement learning. Davide Corsi, Enrico Marchesini, Alessandro Farinelli |
| 2021 | GP-ConvCNP: Better generalization for conditional convolutional Neural Processes on time series data. Jens Petersen, Gregor Köhler, David Zimmerer, Fabian Isensee, Paul F. Jäger, Klaus H. Maier-Hein |
| 2021 | Gaussian process nowcasting: application to COVID-19 mortality reporting. 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 |
| 2021 | Generalization error bounds for deep unfolding RNNs. Boris Joukovsky, Tanmoy Mukherjee, Huynh Van Luong, Nikos Deligiannis |
| 2021 | Generalized parametric path problems. Kshitij Gajjar, Girish Varma, Prerona Chatterjee, Jaikumar Radhakrishnan |
| 2021 | Generating adversarial examples with graph neural networks. Florian Jaeckle, M. Pawan Kumar |
| 2021 | Generative Archimedean copulas. Yuting Ng, Ali Hasan, Khalil Elkhalil, Vahid Tarokh |
| 2021 | Geometric rates of convergence for kernel-based sampling algorithms. Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney |
| 2021 | Global explanations with decision rules: a co-learning approach. Géraldin Nanfack, Paul Temple, Benoît Frénay |
| 2021 | Gradient-based optimization for multi-resource spatial coverage problems. Nitin Kamra, Yan Liu |
| 2021 | Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks. Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande, Vikas Singh |
| 2021 | Graph-based semi-supervised learning through the lens of safety. Shreyas Sheshadri, Avirup Saha, Priyank Patel, Samik Datta, Niloy Ganguly |
| 2021 | Hierarchical Indian buffet neural networks for Bayesian continual learning. Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen J. Roberts |
| 2021 | Hierarchical infinite relational model. Feras A. Saad, Vikash K. Mansinghka |
| 2021 | Hierarchical learning of Hidden Markov Models with clustering regularization. Hui Lan, Antoni B. Chan |
| 2021 | Hierarchical probabilistic model for blind source separation via Legendre transformation. Simon Luo, Lamiae Azizi, Mahito Sugiyama |
| 2021 | High-dimensional Bayesian optimization with sparse axis-aligned subspaces. David Eriksson, Martin Jankowiak |
| 2021 | Identifying regions of trusted predictions. Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner |
| 2021 | Identifying untrustworthy predictions in neural networks by geometric gradient analysis. Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Björn M. Eskofier |
| 2021 | Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces. Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano |
| 2021 | Improving approximate optimal transport distances using quantization. Gaspard Beugnot, Aude Genevay, Kristjan H. Greenewald, Justin Solomon |
| 2021 | Improving uncertainty calibration of deep neural networks via truth discovery and geometric optimization. Chunwei Ma, Ziyun Huang, Jiayi Xian, Mingchen Gao, Jinhui Xu |
| 2021 | Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. Takeshi Teshima, Masashi Sugiyama |
| 2021 | Inference of causal effects when control variables are unknown. Ludvig Hult, Dave Zachariah |
| 2021 | Information theoretic meta learning with Gaussian processes. Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov |
| 2021 | Integer programming-based error-correcting output code design for robust classification. Samarth Gupta, Saurabh Amin |
| 2021 | Invariant representation learning for treatment effect estimation. Claudia Shi, Victor Veitch, David M. Blei |
| 2021 | Investigating vulnerabilities of deep neural policies. Ezgi Korkmaz |
| 2021 | Know your limits: Uncertainty estimation with ReLU classifiers fails at reliable OOD detection. Dennis Ulmer, Giovanni Cinà |
| 2021 | Known unknowns: Learning novel concepts using reasoning-by-elimination. Harsh Agrawal, Eli A. Meirom, Yuval Atzmon, Shie Mannor, Gal Chechik |
| 2021 | Learnable uncertainty under Laplace approximations. Agustinus Kristiadi, Matthias Hein, Philipp Hennig |
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| 2021 | Learning in Multi-Player Stochastic Games. William Brown |
| 2021 | Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging. Renato Lui Geh, Denis Deratani Mauá |
| 2021 | Learning proposals for probabilistic programs with inference combinators. Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem van de Meent |
| 2021 | Learning to learn with Gaussian processes. Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | Leveraging probabilistic circuits for nonparametric multi-output regression. Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting |
| 2021 | Lifted reasoning meets weighted model integration. Jonathan Feldstein, Vaishak Belle |
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| 2021 | LocalNewton: Reducing communication rounds for distributed learning. Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney |
| 2021 | Markov equivalence of max-linear Bayesian networks. Carlos Améndola, Benjamin Hollering, Seth Sullivant, Ngoc Tran |
| 2021 | Matrix games with bandit feedback. Brendan O'Donoghue, Tor Lattimore, Ian Osband |
| 2021 | Maximal ancestral graph structure learning via exact search. Kari Rantanen, Antti Hyttinen, Matti Järvisalo |
| 2021 | Measuring data leakage in machine-learning models with Fisher information. Awni Y. Hannun, Chuan Guo, Laurens van der Maaten |
| 2021 | Min/max stability and box distributions. Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, Andrew McCallum |
| 2021 | Minimax sample complexity for turn-based stochastic game. Qiwen Cui, Lin F. Yang |
| 2021 | Mixed variable Bayesian optimization with frequency modulated kernels. ChangYong Oh, Efstratios Gavves, Max Welling |
| 2021 | Modeling financial uncertainty with multivariate temporal entropy-based curriculums. Ramit Sawhney, Arnav Wadhwa, Ayush Mangal, Vivek Mittal, Shivam Agarwal, Rajiv Ratn Shah |
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| 2021 | Multi-task and meta-learning with sparse linear bandits. Leonardo Cella, Massimiliano Pontil |
| 2021 | NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation. Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao |
| 2021 | Natural language adversarial defense through synonym encoding. Xiaosen Wang, Jin Hao, Yichen Yang, Kun He |
| 2021 | Nearest neighbor search under uncertainty. Blake Mason, Ardhendu Tripathy, Robert Nowak |
| 2021 | Neural markov logic networks. Giuseppe Marra, Ondrej Kuzelka |
| 2021 | No-regret approximate inference via Bayesian optimisation. Rafael Oliveira, Lionel Ott, Fabio Ramos |
| 2021 | No-regret learning with high-probability in adversarial Markov decision processes. Mahsa Ghasemi, Abolfazl Hashemi, Haris Vikalo, Ufuk Topcu |
| 2021 | Non-PSD matrix sketching with applications to regression and optimization. Zhili Feng, Fred Roosta, David P. Woodruff |
| 2021 | On random kernels of residual architectures. Etai Littwin, Tomer Galanti, Lior Wolf |
| 2021 | On the distribution of penultimate activations of classification networks. Minkyo Seo, Yoonho Lee, Suha Kwak |
| 2021 | On the distributional properties of adaptive gradients. Zhiyi Zhang, Ziyin Liu |
| 2021 | On the effects of quantisation on model uncertainty in Bayesian neural networks. Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues |
| 2021 | Optimized auxiliary particle filters: adapting mixture proposals via convex optimization. Nicola Branchini, Víctor Elvira |
| 2021 | PALM: Probabilistic area loss Minimization for Protein Sequence Alignment. Fan Ding, Nan Jiang, Jianzhu Ma, Jian Peng, Jinbo Xu, Yexiang Xue |
| 2021 | PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components. Andrew H. Song, Demba E. Ba, Emery N. Brown |
| 2021 | PROVIDE: a probabilistic framework for unsupervised video decomposition. Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal, Andreas M. Lehrmann |
| 2021 | Partial Identifiability in Discrete Data with Measurement Error. Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser |
| 2021 | Path dependent structural equation models. Ranjani Srinivasan, Jaron J. R. Lee, Rohit Bhattacharya, Ilya Shpitser |
| 2021 | Path-BN: Towards effective batch normalization in the Path Space for ReLU networks. Xufang Luo, Qi Meng, Wei Chen, Yunhong Wang, Tie-Yan Liu |
| 2021 | Possibilistic preference elicitation by minimax regret. Loïc Adam, Sébastien Destercke |
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| 2021 | Preface and Frontmatter. |
| 2021 | Principal component analysis in the stochastic differential privacy model. Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu |
| 2021 | Probabilistic DAG search. Julia Grosse, Cheng Zhang, Philipp Hennig |
| 2021 | Probabilistic selection of inducing points in sparse Gaussian processes. Anders Kirk Uhrenholt, Valentin Charvet, Bjørn Sand Jensen |
| 2021 | Probabilistic task modelling for meta-learning. Cuong Cao Nguyen, Thanh-Toan Do, Gustavo Carneiro |
| 2021 | Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Virtual Event, 27-30 July 2021 Cassio P. de Campos, Marloes H. Maathuis, Erik Quaeghebeur |
| 2021 | RISAN: Robust instance specific deep abstention network. Bhavya Kalra, Kulin Shah, Naresh Manwani |
| 2021 | Random probabilistic circuits. Nicola Di Mauro, Gennaro Gala, Marco Iannotta, Teresa M. A. Basile |
| 2021 | ReZero is all you need: fast convergence at large depth. Thomas Bachlechner, Bodhisattwa Prasad Majumder, Huanru Henry Mao, Gary Cottrell, Julian J. McAuley |
| 2021 | Regstar: efficient strategy synthesis for adversarial patrolling games. David Klaska, Antonín Kucera, Vít Musil, Vojtech Rehák |
| 2021 | Robust principal component analysis for generalized multi-view models. Frank Nussbaum, Joachim Giesen |
| 2021 | Robust reinforcement learning under minimax regret for green security. Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe |
| 2021 | SDM-Net: A simple and effective model for generalized zero-shot learning. Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava |
| 2021 | SGD with low-dimensional gradients with applications to private and distributed learning. Shiva Prasad Kasiviswanathan |
| 2021 | Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting. Adam D. Cobb, Brian Jalaian |
| 2021 | Sequential core-set Monte Carlo. Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell |
| 2021 | Similarity measure for sparse time course data based on Gaussian processes. Zijing Liu, Mauricio Barahona |
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| 2021 | Sketching curvature for efficient out-of-distribution detection for deep neural networks. Apoorva Sharma, Navid Azizan, Marco Pavone |
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| 2021 | Statistically robust neural network classification. Benjie Wang, Stefan Webb, Tom Rainforth |
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| 2021 | The complexity of nonconvex-strongly-concave minimax optimization. Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He |
| 2021 | The curious case of adversarially robust models: More data can help, double descend, or hurt generalization. Yifei Min, Lin Chen, Amin Karbasi |
| 2021 | The neural moving average model for scalable variational inference of state space models. Thomas Ryder, Dennis Prangle, Andrew Golightly, Isaac Matthews |
| 2021 | The promises and pitfalls of deep kernel learning. Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk |
| 2021 | Thompson sampling for Markov games with piecewise stationary opponent policies. Anthony DiGiovanni, Ambuj Tewari |
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| 2021 | Uncertainty-aware sensitivity analysis using Rényi divergences. Topi Paananen, Michael Riis Andersen, Aki Vehtari |
| 2021 | Unsupervised anomaly detection with adversarial mirrored autoencoders. Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi |
| 2021 | Unsupervised constrained community detection via self-expressive graph neural network. Sambaran Bandyopadhyay, Vishal Peter |
| 2021 | Unsupervised program synthesis for images by sampling without replacement. Chenghui Zhou, Chun-Liang Li, Barnabás Póczos |
| 2021 | Variance reduction in frequency estimators via control variates method. Rameshwar Pratap, Raghav Kulkarni |
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| 2021 | Variational inference with continuously-indexed normalizing flows. Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet |
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