
NonAsymptotic Bounds for ZerothOrder Stochastic Optimization
We consider the problem of optimizing an objective function with and wit...
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Estimation of Spectral Risk Measures
We consider the problem of estimating a spectral risk measure (SRM) from...
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Improved Concentration Bounds for Conditional ValueatRisk and Cumulative Prospect Theory using Wasserstein distance
Known finitesample concentration bounds for the Wasserstein distance be...
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Correlated bandits or: How to minimize meansquared error online
While the objective in traditional multiarmed bandit problems is to fin...
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Riskaware Multiarmed Bandits Using Conditional ValueatRisk
Traditional multiarmed bandit problems are geared towards finding the a...
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RiskSensitive Reinforcement Learning: A Constrained Optimization Viewpoint
The classic objective in a reinforcement learning (RL) problem is to fin...
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Random directions stochastic approximation with deterministic perturbations
We introduce deterministic perturbation schemes for the recently propose...
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Concentration bounds for empirical conditional valueatrisk: The unbounded case
In several realworld applications involving decision making under uncer...
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(Bandit) Convex Optimization with Biased Noisy Gradient Oracles
Algorithms for bandit convex optimization and online learning often rely...
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Policy Gradients for CVaRConstrained MDPs
We study a riskconstrained version of the stochastic shortest path (SSP...
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VarianceConstrained ActorCritic Algorithms for Discounted and Average Reward MDPs
In many sequential decisionmaking problems we may want to manage risk b...
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Fast gradient descent for drifting least squares regression, with application to bandits
Online learning algorithms require to often recompute least squares regr...
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Prashanth L. A.
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