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05/24/2023
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Actor-critic (AC) methods are widely used in reinforcement learning (RL)...
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02/06/2023
Target-based Surrogates for Stochastic Optimization
We consider minimizing functions for which it is expensive to compute th...
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07/29/2022
Improved Policy Optimization for Online Imitation Learning
We consider online imitation learning (OIL), where the task is to find a...
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06/13/2022
Near-Optimal Sample Complexity Bounds for Constrained MDPs
In contrast to the advances in characterizing the sample complexity for ...
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04/11/2022
Towards Painless Policy Optimization for Constrained MDPs
We study policy optimization in an infinite horizon, γ-discounted constr...
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10/21/2021
Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent
We design step-size schemes that make stochastic gradient descent (SGD) ...
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08/12/2021
A functional mirror ascent view of policy gradient methods with function approximation
We use functional mirror ascent to propose a general framework (referred...
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02/18/2021
SVRG Meets AdaGrad: Painless Variance Reduction
Variance reduction (VR) methods for finite-sum minimization typically re...
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06/11/2020
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search)
As adaptive gradient methods are typically used for training over-parame...
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02/24/2020
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
We propose a stochastic variant of the classical Polyak step-size (Polya...
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10/11/2019
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
We propose RandUCB, a bandit strategy that uses theoretically derived co...
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10/11/2019
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation
We consider stochastic second order methods for minimizing strongly-conv...
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05/24/2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Recent works have shown that stochastic gradient descent (SGD) achieves ...
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10/16/2018
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron
Modern machine learning focuses on highly expressive models that are abl...
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10/10/2018
Combining Bayesian Optimization and Lipschitz Optimization
Bayesian optimization and Lipschitz optimization have developed alternat...
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05/24/2018
New Insights into Bootstrapping for Bandits
We investigate the use of bootstrapping in the bandit setting. We first ...
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05/21/2016
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
We study the stochastic online problem of learning to influence in a soc...
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02/27/2015
Influence Maximization with Bandits
We consider the problem of influence maximization, the problem of maximi...
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10/24/2013
