21 citations · 59 across the 19 of their papers we have counts for
8 papers · 1 filter
Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach
James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras
In order for reinforcement learning techniques to be useful in real-world decision making processes, they must be able to produce robust performance from limited data. Deep policy…
Provable Hierarchical Imitation Learning via EM
Zhiyu Zhang, Ioannis Paschalidis
Due to recent empirical successes, the options framework for hierarchical reinforcement learning is gaining increasing popularity. Rather than learning from rewards which suffers f…
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case
Salomón Wollenstein-Betech, Christian Muise, Christos G. Cassandras +2
Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainab…
Robust Grouped Variable Selection Using Distributionally Robust Optimization
Ruidi Chen, Ioannis Ch. Paschalidis
We propose a Distributionally Robust Optimization (DRO) formulation with a Wasserstein-based uncertainty set for selecting grouped variables under perturbations on the data for bot…
Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric
Ruidi Chen, Ioannis Ch. Paschalidis
We develop Distributionally Robust Optimization (DRO) formulations for Multivariate Linear Regression (MLR) and Multiclass Logistic Regression (MLG) when both the covariates and re…
Local SGD With a Communication Overhead Depending Only on the Number of Workers
Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis
We consider speeding up stochastic gradient descent (SGD) by parallelizing it across multiple workers. We assume the same data set is shared among workers, who can take SGD ste…