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07/03/2023
Monte Carlo Policy Gradient Method for Binary Optimization
Binary optimization has a wide range of applications in combinatorial op...
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03/19/2023
Provable Convergence of Variational Monte Carlo Methods
The Variational Monte Carlo (VMC) is a promising approach for computing ...
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02/25/2023
Provably Efficient Gauss-Newton Temporal Difference Learning Method with Function Approximation
In this paper, based on the spirit of Fitted Q-Iteration (FQI), we propo...
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09/26/2022
Some Sharp Error Bounds for Multivariate Linear Interpolation and Extrapolation
We study in this paper the function approximation error of linear interp...
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07/15/2022
Riemannian Natural Gradient Methods
This paper studies large-scale optimization problems on Riemannian manif...
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07/13/2022
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
As an important framework for safe Reinforcement Learning, the Constrain...
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06/14/2021
NG+ : A Multi-Step Matrix-Product Natural Gradient Method for Deep Learning
In this paper, a novel second-order method called NG+ is proposed. By fo...
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05/20/2021
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning
In this paper, we consider the linear programming (LP) formulation for d...
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06/17/2020
Structured Stochastic Quasi-Newton Methods for Large-Scale Optimization Problems
In this paper, we consider large-scale finite-sum nonconvex problems ari...
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06/10/2020
Sketchy Empirical Natural Gradient Methods for Deep Learning
In this paper, we develop an efficient sketchy empirical natural gradien...
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07/02/2019
Ground states and their characterization of spin-F Bose-Einstein condensates
The computation of the ground states of spin-F Bose-Einstein condensates...
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06/09/2019
Toward Solving 2-TBSG Efficiently
2-TBSG is a two-player game model which aims to find Nash equilibriums a...
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10/14/2018
Adaptive Low-Nonnegative-Rank Approximation for State Aggregation of Markov Chains
This paper develops a low-nonnegative-rank approximation method to ident...
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03/09/2018