Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent J Altschuler, S Chewi, PR Gerber, A Stromme Advances in Neural Information Processing Systems 34, 22132-22145, 2021 | 36 | 2021 |
The query complexity of sampling from strongly log-concave distributions in one dimension S Chewi, PR Gerber, C Lu, T Le Gouic, P Rigollet Conference on Learning Theory, 2041-2059, 2022 | 13 | 2022 |
Fisher information lower bounds for sampling S Chewi, P Gerber, H Lee, C Lu International Conference on Algorithmic Learning Theory, 375-410, 2023 | 11 | 2023 |
Rejection sampling from shape-constrained distributions in sublinear time S Chewi, PR Gerber, C Lu, T Le Gouic, P Rigollet International conference on artificial intelligence and statistics, 2249-2265, 2022 | 5 | 2022 |
Solving OpenAI’s Car Racing Environment with Deep Reinforcement Learning and Dropout P Gerber, J Guan, E Nunez, K Phamdo, T Monsoor, N Malaya https://github.com/AMD-RIPS/RL-2018, 2018 | 4 | 2018 |
Likelihood-free hypothesis testing PR Gerber, Y Polyanskiy arXiv preprint arXiv:2211.01126, 2022 | 2 | 2022 |
Minimax optimal testing by classification PR Gerber, Y Han, Y Polyanskiy The Thirty Sixth Annual Conference on Learning Theory, 5395-5432, 2023 | 1 | 2023 |
Gaussian discrepancy: A probabilistic relaxation of vector balancing S Chewi, P Gerber, P Rigollet, P Turner Discrete Applied Mathematics 322, 123-141, 2022 | 1 | 2022 |
Kernel-Based Tests for Likelihood-Free Hypothesis Testing PR Gerber, T Jiang, Y Polyanskiy, R Sun Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
Density estimation using the perceptron PR Gerber, T Jiang, Y Polyanskiy, R Sun arXiv preprint arXiv:2312.17701, 2023 | | 2023 |