| 2020 | A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates. | Zhixian Lei, Kyle Luh, Prayaag Venkat, Fred Zhang |
| 2020 | Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo. | Yin Tat Lee, Ruoqi Shen, Kevin Tian |
| 2020 | An $\widetilde\mathcalO(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints. | Yin Tat Lee, Swati Padmanabhan |
| 2020 | A Closer Look at Small-loss Bounds for Bandits with Graph Feedback. | Chung-Wei Lee, Haipeng Luo, Mengxiao Zhang |
| 2020 | Exploration by Optimisation in Partial Monitoring. | Tor Lattimore, Csaba Szepesvri |
| 2020 | The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians. | Jeongyeol Kwon, Constantine Caramanis |
| 2020 | On Suboptimality of Least Squares with Application to Estimation of Convex Bodies. | Gil Kur, Alexander Rakhlin, Adityanand Guntuboyina |
| 2020 | Open Problem: Tight Convergence of SGD in Constant Dimension. | Tomer Koren, Shahar Segal |
| 2020 | New Potential-Based Bounds for Prediction with Expert Advice. | Vladimir A. Kobzar, Robert V. Kohn, Zhilei Wang |
| 2020 | Information Directed Sampling for Linear Partial Monitoring. | Johannes Kirschner, Tor Lattimore, Andreas Krause |
| 2020 | Universal Approximation with Deep Narrow Networks. | Patrick Kidger, Terry J. Lyons |
| 2020 | Online Learning with Vector Costs and Bandits with Knapsacks. | Thomas Kesselheim, Sahil Singla |
| 2020 | Privately Learning Thresholds: Closing the Exponential Gap. | Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer |
| 2020 | Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. | Pritish Kamath, Omar Montasser, Nathan Srebro |
| 2020 | Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. | Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai |
| 2020 | Provably efficient reinforcement learning with linear function approximation. | Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan |
| 2020 | Gradient descent follows the regularization path for general losses. | Ziwei Ji, Miroslav Dudk, Robert E. Schapire, Matus Telgarsky |
| 2020 | Efficient improper learning for online logistic regression. | Rmi Jzquel, Pierre Gaillard, Alessandro Rudi |
| 2020 | Robust causal inference under covariate shift via worst-case subpopulation treatment effects. | Sookyo Jeong, Hongseok Namkoong |
| 2020 | Precise Tradeoffs in Adversarial Training for Linear Regression. | Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani |
| 2020 | Extrapolating the profile of a finite population. | Soham Jana, Yury Polyanskiy, Yihong Wu |
| 2020 | Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. | Yichun Hu, Nathan Kallus, Xiaojie Mao |
| 2020 | Noise-tolerant, Reliable Active Classification with Comparison Queries. | Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan |
| 2020 | A Greedy Anytime Algorithm for Sparse PCA. | Guy Holtzman, Adam Soffer, Dan Vilenchik |
| 2020 | Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. | Oliver Hinder, Aaron Sidford, Nimit Sharad Sohoni |