41 citations · 267 across the 66 of their papers we have counts for
6 papers · 1 filter
Closing the gap between SVRG and TD-SVRG with Gradient Splitting
Arsenii Mustafin, Alex Olshevsky, Ioannis Ch. Paschalidis
Temporal difference (TD) learning is a policy evaluation in reinforcement learning whose performance can be enhanced by variance reduction methods. Recently, multiple works have so…
Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers
Ruidi Chen, Boran Hao, Ioannis Ch. Paschalidis
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework…
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation
Vittorio Giammarino, James Queeney, Lucas C. Carstensen +2
We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theor…
Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras
We develop a new class of model-free deep reinforcement learning algorithms for data-driven, learning-based control. Our Generalized Policy Improvement algorithms combine the polic…
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task
Vittorio Giammarino, Matthew F Dunne, Kylie N Moore +3
We develop a simple framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging envir…
PDE-Based Optimal Strategy for Unconstrained Online Learning
Zhiyu Zhang, Ashok Cutkosky, Ioannis Paschalidis
Unconstrained Online Linear Optimization (OLO) is a practical problem setting to study the training of machine learning models. Existing works proposed a number of potential-based…