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04/21/2023
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning
We consider the problem of control in the setting of reinforcement learn...
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12/20/2022
Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias
We present in this paper a family of generalized simultaneous perturbati...
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10/12/2022
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
We study the finite-time behaviour of the popular temporal difference (T...
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07/30/2022
A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization
In this paper, we present a stochastic gradient algorithm for minimizing...
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05/12/2022
A Survey of Risk-Aware Multi-Armed Bandits
In several applications such as clinical trials and financial portfolio ...
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03/31/2022
Adaptive Estimation of Random Vectors with Bandit Feedback
We consider the problem of sequentially learning to estimate, in the mea...
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02/22/2022
Approximate gradient ascent methods for distortion risk measures
We propose approximate gradient ascent algorithms for risk-sensitive rei...
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11/16/2021
Online Estimation and Optimization of Utility-Based Shortfall Risk
Utility-Based Shortfall Risk (UBSR) is a risk metric that is increasingl...
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07/09/2021
Likelihood ratio-based policy gradient methods for distorted risk measures: A non-asymptotic analysis
We propose policy-gradient algorithms for solving the problem of control...
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01/06/2021
Smoothed functional-based gradient algorithms for off-policy reinforcement learning
We consider the problem of control in an off-policy reinforcement learni...
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02/26/2020
Non-Asymptotic Bounds for Zeroth-Order Stochastic Optimization
We consider the problem of optimizing an objective function with and wit...
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12/22/2019
Estimation of Spectral Risk Measures
We consider the problem of estimating a spectral risk measure (SRM) from...
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02/27/2019
Improved Concentration Bounds for Conditional Value-at-Risk and Cumulative Prospect Theory using Wasserstein distance
Known finite-sample concentration bounds for the Wasserstein distance be...
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02/08/2019
Correlated bandits or: How to minimize mean-squared error online
While the objective in traditional multi-armed bandit problems is to fin...
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01/04/2019
Risk-aware Multi-armed Bandits Using Conditional Value-at-Risk
Traditional multi-armed bandit problems are geared towards finding the a...
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10/22/2018
Risk-Sensitive Reinforcement Learning: A Constrained Optimization Viewpoint
The classic objective in a reinforcement learning (RL) problem is to fin...
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08/08/2018
Random directions stochastic approximation with deterministic perturbations
We introduce deterministic perturbation schemes for the recently propose...
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08/06/2018
Concentration bounds for empirical conditional value-at-risk: The unbounded case
In several real-world applications involving decision making under uncer...
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09/22/2016
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles
Algorithms for bandit convex optimization and online learning often rely...
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05/12/2014
Policy Gradients for CVaR-Constrained MDPs
We study a risk-constrained version of the stochastic shortest path (SSP...
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03/25/2014
Variance-Constrained Actor-Critic Algorithms for Discounted and Average Reward MDPs
In many sequential decision-making problems we may want to manage risk b...
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07/11/2013