3 citations · 4 across the 7 of their papers we have counts for
7 papers
Mini-Batch Risk-Averse Deep Q-Learning: A Robot Navigation Case Study
Aayush Patel, Andrzej Ruszczyński
We study the control of Markov decision processes in which the quality of a policy is evaluated by a dynamic, time-consistent Markov risk measure rather than by an expected discoun…
Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation
Andrzej Ruszczynski, Tiangang Zhang
For a risk-averse finite-horizon Markov Decision Problem, we introduce a special class of Markov coherent risk measures, called mini-batch measures. We also define the class of mul…
Convergence Rate of a Functional Learning Method for Contextual Stochastic Optimization
Noel Smith, Andrzej Ruszczynski
We consider a stochastic optimization problem involving two random variables: a context variable and a dependent variable . The objective is to minimize the expected value o…
Federated Calculation of the Free-Support Transportation Barycenter by Single-Loop Dual Decomposition
Zhengqi Lin, Andrzej Ruszczyński
We propose an efficient federated dual decomposition algorithm for calculating the Wasserstein barycenter of several distributions, including choosing the support of the solution.…
A Functional Model Method for Nonconvex Nonsmooth Conditional Stochastic Optimization
Andrzej Ruszczyński, Shangzhe Yang
We consider stochastic optimization problems involving an expected value of a nonlinear function of a base random vector and a conditional expectation of another function depending…
Fast Dual Subgradient Optimization of the Integrated Transportation Distance Between Stochastic Kernels
Zhengqi Lin, Andrzej Ruszczynski
A generalization of the Wasserstein metric, the integrated transportation distance, establishes a novel distance between probability kernels of Markov systems. This metric serves a…
An Integrated Transportation Distance Between Kernels and Approximate Dynamic Risk Evaluation in Markov Systems
Zhengqi Lin, Andrzej Ruszczynski
We introduce a distance between kernels based on the Wasserstein distances between their values, study its properties, and prove that it is a metric on an appropriately defined spa…