| 2021 | Hierarchical probabilistic model for blind source separation via Legendre transformation. | Simon Luo, Lamiae Azizi, Mahito Sugiyama |
| 2021 | Strategically efficient exploration in competitive multi-agent reinforcement learning. | Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann |
| 2021 | Tractable computation of expected kernels. | Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck |
| 2021 | Similarity measure for sparse time course data based on Gaussian processes. | Zijing Liu, Mauricio Barahona |
| 2021 | On random kernels of residual architectures. | Etai Littwin, Tomer Galanti, Lior Wolf |
| 2021 | Bayesian optimization for modular black-box systems with switching costs. | Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer |
| 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 | Dimension reduction for data with heterogeneous missingness. | Yurong Ling, Zijing Liu, Jing-Hao Xue |
| 2021 | An unsupervised video game playstyle metric via state discretization. | Chiu-Chou Lin, Wei-Chen Chiu, I-Chen Wu |
| 2021 | CLAIM: curriculum learning policy for influence maximization in unknown social networks. | Dexun Li, Meghna Lowalekar, Pradeep Varakantham |
| 2021 | Convergence behavior of belief propagation: estimating regions of attraction via Lyapunov functions. | Harald Leisenberger, Christian Knoll, Richard Seeber, Franz Pernkopf |
| 2021 | A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. | Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger |
| 2021 | Hierarchical learning of Hidden Markov Models with clustering regularization. | Hui Lan, Antoni B. Chan |
| 2021 | Disentangling mixtures of unknown causal interventions. | Abhinav Kumar, Gaurav Sinha |
| 2021 | Learnable uncertainty under Laplace approximations. | Agustinus Kristiadi, Matthias Hein, Philipp Hennig |
| 2021 | Trumpets: Injective flows for inference and inverse problems. | Konik Kothari, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanic |
| 2021 | Investigating vulnerabilities of deep neural policies. | Ezgi Korkmaz |
| 2021 | Stochastic model for sunk cost bias. | Jon M. Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar |
| 2021 | Regstar: efficient strategy synthesis for adversarial patrolling games. | David Klaska, Antonn Kucera, Vt Musil, Vojtech Rehk |
| 2021 | pRSL: Interpretable multi-label stacking by learning probabilistic rules. | Michael Kirchhof, Lena Schmid, Christopher Reining, Michael ten Hompel, Markus Pauly |
| 2021 | Geometric rates of convergence for kernel-based sampling algorithms. | Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney |
| 2021 | Hierarchical Indian buffet neural networks for Bayesian continual learning. | Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen J. Roberts |
| 2021 | Constrained differentially private federated learning for low-bandwidth devices. | Raouf Kerkouche, Gergely cs, Claude Castelluccia, Pierre Genevs |
| 2021 | Approximate implication with d-separation. | Batya Kenig |
| 2021 | SGD with low-dimensional gradients with applications to private and distributed learning. | Shiva Prasad Kasiviswanathan |