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07/29/2023
First-order Policy Optimization for Robust Policy Evaluation
We adopt a policy optimization viewpoint towards policy evaluation for r...
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07/04/2023
Accelerated stochastic approximation with state-dependent noise
We consider a class of stochastic smooth convex optimization problems un...
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03/28/2023
Numerical Methods for Convex Multistage Stochastic Optimization
Optimization problems involving sequential decisions in a stochastic env...
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03/08/2023
Policy Mirror Descent Inherently Explores Action Space
Designing computationally efficient exploration strategies for on-policy...
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11/30/2022
Policy Optimization over General State and Action Spaces
Reinforcement learning (RL) problems over general state and action space...
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10/11/2022
Functional Constrained Optimization for Risk Aversion and Sparsity Control
Risk and sparsity requirements often need to be enforced simultaneously ...
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09/21/2022
First-order Policy Optimization for Robust Markov Decision Process
We consider the problem of solving robust Markov decision process (MDP),...
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05/11/2022
Stochastic first-order methods for average-reward Markov decision processes
We study the problem of average-reward Markov decision processes (AMDPs)...
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02/16/2022
Data-Driven Minimax Optimization with Expectation Constraints
Attention to data-driven optimization approaches, including the well-kno...
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01/24/2022
Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity
We propose the homotopic policy mirror descent (HPMD) method for solving...
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01/15/2022
Block Policy Mirror Descent
In this paper, we present a new class of policy gradient (PG) methods, n...
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12/24/2021
Accelerated and instance-optimal policy evaluation with linear function approximation
We study the problem of policy evaluation with linear function approxima...
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10/20/2021
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process
The problem of constrained Markov decision process (CMDP) is investigate...
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10/10/2021
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits
Embedding learning has found widespread applications in recommendation s...
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01/30/2021
Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classes
We present new policy mirror descent (PMD) methods for solving reinforce...
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11/15/2020
Simple and optimal methods for stochastic variational inequalities, II: Markovian noise and policy evaluation in reinforcement learning
The focus of this paper is on stochastic variational inequalities (VI) u...
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11/11/2020
A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis
Safe reinforcement learning (SRL) problems are typically modeled as cons...
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11/05/2020
Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation
In this paper we first present a novel operator extrapolation (OE) metho...
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10/23/2020
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Nonconvex sparse models have received significant attention in high-dime...
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06/30/2020
Conditional Gradient Methods for convex optimization with function constraints
Conditional gradient methods have attracted much attention in both machi...
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06/03/2020
A Unified Single-loop Alternating Gradient Projection Algorithm for Nonconvex-Concave and Convex-Nonconcave Minimax Problems
Much recent research effort has been directed to the development of effi...
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12/16/2019
Complexity of Stochastic Dual Dynamic Programming
Stochastic dual dynamic programming is a cutting plane type algorithm fo...
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08/07/2019
Proximal Point Methods for Optimization with Nonconvex Functional Constraints
Nonconvex optimization is becoming more and more important in machine le...
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06/01/2019
GLAD: Learning Sparse Graph Recovery
Recovering sparse conditional independence graphs from data is a fundame...
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05/29/2019
A unified variance-reduced accelerated gradient method for convex optimization
We propose a novel randomized incremental gradient algorithm, namely, VA...
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10/09/2018
Cubic Regularization with Momentum for Nonconvex Optimization
Momentum is a popular technique to accelerate the convergence in practic...
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10/01/2018
Optimal Adaptive and Accelerated Stochastic Gradient Descent
Stochastic gradient descent (Sgd) methods are the most powerful optimiza...
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09/27/2018
Complexity of Training ReLU Neural Network
In this paper, we explore some basic questions on the complexity of trai...
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09/24/2018
Asynchronous decentralized accelerated stochastic gradient descent
In this work, we introduce an asynchronous decentralized accelerated sto...
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08/22/2018
A Note on Inexact Condition for Cubic Regularized Newton's Method
This note considers the inexact cubic-regularized Newton's method (CR), ...
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02/20/2018
Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization
The popular cubic regularization (CR) method converges with first- and s...
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11/15/2017
Random gradient extrapolation for distributed and stochastic optimization
In this paper, we consider a class of finite-sum convex optimization pro...
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07/11/2017
Dynamic Stochastic Approximation for Multi-stage Stochastic Optimization
In this paper, we consider multi-stage stochastic optimization problems ...
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03/16/2017
Conditional Accelerated Lazy Stochastic Gradient Descent
In this work we introduce a conditional accelerated lazy stochastic grad...
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04/13/2016
Algorithms for stochastic optimization with expectation constraints
This paper considers the problem of minimizing an expectation function o...
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08/29/2015
Generalized Uniformly Optimal Methods for Nonlinear Programming
In this paper, we present a generic framework to extend existing uniform...
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07/08/2015
An optimal randomized incremental gradient method
In this paper, we consider a class of finite-sum convex optimization pro...
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09/22/2013
