21 citations · 59 across the 15 of their papers we have counts for
5 papers · 1 filter
Generalized Proximal Policy Optimization with Sample Reuse
James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras
In real-world decision making tasks, it is critical for data-driven reinforcement learning methods to be both stable and sample efficient. On-policy methods typically generate reli…
Distributionally Robust Multi-Output Regression Ranking
Shahabeddin Sotudian, Ruidi Chen, Ioannis Paschalidis
Despite their empirical success, most existing listwiselearning-to-rank (LTR) models are not built to be robust to errors in labeling or annotation, distributional data shift, or a…
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…
Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach
Theodora S. Brisimi, Tingting Xu, Taiyao Wang +3
Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic dise…