| 2019 | Approximate Guarantees for Dictionary Learning. | Aditya Bhaskara, Wai Ming Tai |
| 2019 | Lower bounds for testing graphical models: colorings and antiferromagnetic Ising models. | Ivona Bezkov, Antonio Blanca, Zongchen Chen, Daniel Stefankovic, Eric Vigoda |
| 2019 | Private Center Points and Learning of Halfspaces. | Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer |
| 2019 | Learning Two Layer Rectified Neural Networks in Polynomial Time. | Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff |
| 2019 | A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise. | Francis R. Bach, Kfir Y. Levy |
| 2019 | Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes. | Peter Auer, Pratik Gajane, Ronald Ortner |
| 2019 | Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information. | Peter Auer, Yifang Chen, Pratik Gajane, Chung-Wei Lee, Haipeng Luo, Ronald Ortner, Chen-Yu Wei |
| 2019 | Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. | Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu |
| 2019 | Towards Testing Monotonicity of Distributions Over General Posets. | Maryam Aliakbarpour, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee |
| 2019 | Testing Mixtures of Discrete Distributions. | Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld |
| 2019 | Learning to Prune: Speeding up Repeated Computations. | Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik |
| 2019 | Learning in Non-convex Games with an Optimization Oracle. | Naman Agarwal, Alon Gonen, Elad Hazan |
| 2019 | Inference under Information Constraints: Lower Bounds from Chi-Square Contraction. | Jayadev Acharya, Clment L. Canonne, Himanshu Tyagi |
| 2019 | An Optimal High-Order Tensor Method for Convex Optimization. | Bo Jiang, Haoyue Wang, Shuzhong Zhang |
| 2019 | Making the Last Iterate of SGD Information Theoretically Optimal. | Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli |
| 2019 | Faster Algorithms for High-Dimensional Robust Covariance Estimation. | Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff |
| 2019 | Open Problem: Do Good Algorithms Necessarily Query Bad Points? | Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli |
| 2018 | An Estimate Sequence for Geodesically Convex Optimization. | Hongyi Zhang, Suvrit Sra |
| 2018 | Efficient active learning of sparse halfspaces. | Chicheng Zhang |
| 2018 | Optimal approximation of continuous functions by very deep ReLU networks. | Dmitry Yarotsky |
| 2018 | Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem. | Andre Wibisono |
| 2018 | More Adaptive Algorithms for Adversarial Bandits. | Chen-Yu Wei, Haipeng Luo |
| 2018 | An explicit analysis of the entropic penalty in linear programming. | Jonathan Weed |
| 2018 | Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. | Belinda Tzen, Tengyuan Liang, Maxim Raginsky |
| 2018 | Private Sequential Learning. | John N. Tsitsiklis, Kuang Xu, Zhi Xu |