3 citations · 6 across the 8 of their papers we have counts for
5 papers · 1 filter
Optimizing Shortfall Risk Metric for Learning Regression Models
Harish G. Ramaswamy, L. A. Prashanth
We consider the problem of estimating and optimizing utility-based shortfall risk (UBSR) of a loss, say , in the context of a regression problem. Empirical risk min…
Generalized Simultaneous Perturbation-based Gradient Search with Reduced Estimator Bias
Soumen Pachal, Shalabh Bhatnagar, L. A. Prashanth
We present in this paper a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function mea…
Minimum mean-squared error estimation with bandit feedback
Ayon Ghosh, L. A. Prashanth, Dipayan Sen +1
We consider the problem of sequentially learning to estimate, in the mean squared error (MSE) sense, a Gaussian -vector of unknown covariance by observing only of its en…
Variance-Constrained Actor-Critic Algorithms for Discounted and Average Reward MDPs
Prashanth L. A., Mohammad Ghavamzadeh
In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in rewards in addition to maximizing a standard criterion. Variance…
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling
L. A. Prashanth, Nathaniel Korda, Rémi Munos
We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our propos…