research
∙
02/27/2022
Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming
We propose a framework, called neural-progressive hedging (NP), that lev...
share
research
∙
10/14/2021
Order Constraints in Optimal Transport
Optimal transport is a framework for comparing measures whereby a cost i...
research
∙
02/18/2021
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning
One of the main challenges in real-world reinforcement learning is to le...
research
∙
09/14/2020
Fed+: A Family of Fusion Algorithms for Federated Learning
We present a class of methods for federated learning, which we call Fed+...
research
∙
06/01/2020
Variational Bayesian Inference for Crowdsourcing Predictions
Crowdsourcing has emerged as an effective means for performing a number ...
research
∙
04/03/2020
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control
Air traffic control is an example of a highly challenging operational pr...
research
∙
02/14/2012