| Generalization bounds of sgld for non-convex learning: Two theoretical viewpoints W Mou, L Wang, X Zhai, K Zheng Conference on Learning Theory, 605-638, 2018 | 151 | 2018 |
| Efficient private ERM for smooth objectives J Zhang, K Zheng, W Mou, L Wang arXiv preprint arXiv:1703.09947, 2017 | 150 | 2017 |
| Differentially private clustering in high-dimensional euclidean spaces MF Balcan, T Dick, Y Liang, W Mou, H Zhang International Conference on Machine Learning, 322-331, 2017 | 96 | 2017 |
| High-order Langevin diffusion yields an accelerated MCMC algorithm W Mou, YA Ma, MJ Wainwright, PL Bartlett, MI Jordan Journal of Machine Learning Research 22 (42), 1-41, 2021 | 88 | 2021 |
| On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration W Mou, CJ Li, MJ Wainwright, PL Bartlett, MI Jordan Conference on Learning Theory, 2947-2997, 2020 | 87 | 2020 |
| Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity W Mou, N Flammarion, MJ Wainwright, PL Bartlett Bernoulli 28 (3), 1577-1601, 2022 | 71 | 2022 |
| Collect at once, use effectively: Making non-interactive locally private learning possible K Zheng, W Mou, L Wang International Conference on Machine Learning, 4130-4139, 2017 | 50 | 2017 |
| Dropout training, data-dependent regularization, and generalization bounds W Mou, Y Zhou, J Gao, L Wang International conference on machine learning, 3645-3653, 2018 | 34 | 2018 |
| An efficient sampling algorithm for non-smooth composite potentials W Mou, N Flammarion, MJ Wainwright, PL Bartlett Journal of Machine Learning Research 23 (233), 1-50, 2022 | 27 | 2022 |
| Optimal oracle inequalities for projected fixed-point equations, with applications to policy evaluation W Mou, A Pananjady, MJ Wainwright Mathematics of Operations Research 48 (4), 2308-2336, 2023 | 26* | 2023 |
| Optimal and instance-dependent guarantees for Markovian linear stochastic approximation W Mou, A Pananjady, MJ Wainwright, PL Bartlett Mathematical Statistics and Learning 7 (1), 41-153, 2024 | 24 | 2024 |
| Root-sgd: Sharp nonasymptotics and asymptotic efficiency in a single algorithm CJ Li, W Mou, M Wainwright, M Jordan Conference on Learning Theory, 909-981, 2022 | 20 | 2022 |
| A Diffusion Process Perspective on Posterior Contraction Rates for Parameters W Mou, N Ho, M Wainwright, P Bartlett, M Jordan SIAM Journal on Mathematics of Data Science 6 (2), 553-577, 2024 | 18* | 2024 |
| On the sample complexity of reinforcement learning with policy space generalization W Mou, Z Wen, X Chen arXiv preprint arXiv:2008.07353, 2020 | 12 | 2020 |
| Optimal variance-reduced stochastic approximation in Banach spaces W Mou, K Khamaru, MJ Wainwright, PL Bartlett, MI Jordan arXiv preprint arXiv:2201.08518, 2022 | 11 | 2022 |
| Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency W Mou, P Ding, MJ Wainwright, PL Bartlett arXiv preprint arXiv:2301.06240, 2023 | 10 | 2023 |
| Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency W Mou, MJ Wainwright, PL Bartlett arXiv preprint arXiv:2209.13075, 2022 | 10 | 2022 |
| The impact of local oscillator frequency jitter and laser linewidth to ultra high baud rate coherent systems R Zhang, WJ Jiang, K Kuzmin, Y Weng, W Mou, GK Chang, WI Way Journal of Lightwave Technology 38 (6), 1138-1147, 2020 | 10 | 2020 |
| Sampling for bayesian mixture models: Mcmc with polynomial-time mixing W Mou, N Ho, MJ Wainwright, PL Bartlett, MI Jordan arXiv preprint arXiv:1912.05153, 2019 | 10* | 2019 |
| When is the estimated propensity score better? High-dimensional analysis and bias correction F Su, W Mou, P Ding, MJ Wainwright arXiv preprint arXiv:2303.17102, 2023 | 8 | 2023 |
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyVerified email at mit.edu
