no code implementations • 8 Jul 2024 • Wenlong Mou, Yuhua Zhu

We study the problem of computing the value function from a discretely-observed trajectory of a continuous-time diffusion process.

no code implementations • 16 Jan 2023 • Wenlong Mou, Peng Ding, Martin J. Wainwright, Peter L. Bartlett

When it is violated, the classical semi-parametric efficiency bound can easily become infinite, so that the instance-optimal risk depends on the function class used to model the regression function.

no code implementations • 26 Sep 2022 • Wenlong Mou, Martin J. Wainwright, Peter L. Bartlett

The problem of estimating a linear functional based on observational data is canonical in both the causal inference and bandit literatures.

no code implementations • 21 Jan 2022 • Wenlong Mou, Koulik Khamaru, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan

We study the problem of estimating the fixed point of a contractive operator defined on a separable Banach space.

no code implementations • 23 Dec 2021 • Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, Peter L. Bartlett

We then prove a non-asymptotic instance-dependent bound on a suitably averaged sequence of iterates, with a leading term that matches the local asymptotic minimax limit, including sharp dependence on the parameters $(d, t_{\mathrm{mix}})$ in the higher order terms.

no code implementations • 9 Dec 2020 • Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright

Linear fixed point equations in Hilbert spaces arise in a variety of settings, including reinforcement learning, and computational methods for solving differential and integral equations.

no code implementations • 17 Aug 2020 • Wenlong Mou, Zheng Wen, Xi Chen

To avoid such undesirable dependence on the state and action space sizes, this paper proposes a new notion of eluder dimension for the policy space, which characterizes the intrinsic complexity of policy learning in an arbitrary Markov Decision Process (MDP).

no code implementations • 9 Apr 2020 • Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael. I. Jordan

When the matrix $\bar{A}$ is Hurwitz, we prove a central limit theorem (CLT) for the averaged iterates with fixed step size and number of iterations going to infinity.

no code implementations • 11 Dec 2019 • Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael. I. Jordan

We study the problem of sampling from the power posterior distribution in Bayesian Gaussian mixture models, a robust version of the classical posterior.

no code implementations • 1 Oct 2019 • Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett

We consider the problem of sampling from a density of the form $p(x) \propto \exp(-f(x)- g(x))$, where $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a smooth and strongly convex function and $g: \mathbb{R}^d \rightarrow \mathbb{R}$ is a convex and Lipschitz function.

no code implementations • 28 Aug 2019 • Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael. I. Jordan

We propose a Markov chain Monte Carlo (MCMC) algorithm based on third-order Langevin dynamics for sampling from distributions with log-concave and smooth densities.

no code implementations • 25 Jul 2019 • Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett

We present an improved analysis of the Euler-Maruyama discretization of the Langevin diffusion.

no code implementations • ICML 2018 • Wenlong Mou, Yuchen Zhou, Jun Gao, Li-Wei Wang

We study the problem of generalization guarantees for dropout training.

no code implementations • ICML 2017 • Maria-Florina Balcan, Travis Dick, YIngyu Liang, Wenlong Mou, Hongyang Zhang

We study the problem of clustering sensitive data while preserving the privacy of individuals represented in the dataset, which has broad applications in practical machine learning and data analysis tasks.

no code implementations • 19 Jul 2017 • Wenlong Mou, Li-Wei Wang, Xiyu Zhai, Kai Zheng

This is the first algorithm-dependent result with reasonable dependence on aggregated step sizes for non-convex learning, and has important implications to statistical learning aspects of stochastic gradient methods in complicated models such as deep learning.

no code implementations • ICML 2017 • Kai Zheng, Wenlong Mou, Li-Wei Wang

For learning with smooth generalized linear losses, we propose an approximate stochastic gradient oracle estimated from non-interactive LDP channel, using Chebyshev expansion.

no code implementations • 29 Mar 2017 • Jiaqi Zhang, Kai Zheng, Wenlong Mou, Li-Wei Wang

For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both $\epsilon$-DP and $(\epsilon, \delta)$-DP.

no code implementations • 14 Dec 2016 • Wenlong Mou, Zhi Wang, Li-Wei Wang

In Valiant's neuroidal model, the hippocampus was described as a randomly connected graph, the computation on which maps input to a set of activated neuroids with stable size.

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