112 citations · 149 across the 11 of their papers we have counts for
14 papers
Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming
Supriyo Ghosh, Laura Wynter, Shiau Hong Lim +1
We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The…
Decentralized Deterministic Multi-Agent Reinforcement Learning
Antoine Grosnit, Desmond Cai, Laura Wynter
[Zhang, ICML 2018] provided the first decentralized actor-critic algorithm for multi-agent reinforcement learning (MARL) that offers convergence guarantees. In that work, policies…
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning
Desmond Cai, Shiau Hong Lim, Laura Wynter
One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples. We show that in certain settings, the available data can be…
Probabilistic Inference for Learning from Untrusted Sources
Duc Thien Nguyen, Shiau Hoong Lim, Laura Wynter +1
Federated learning brings potential benefits of faster learning, better solutions, and a greater propensity to transfer when heterogeneous data from different parties increases div…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
Variational Bayesian Inference for Crowdsourcing Predictions
Desmond Cai, Duc Thien Nguyen, Shiau Hong Lim +1
Crowdsourcing has emerged as an effective means for performing a number of machine learning tasks such as annotation and labelling of images and other data sets. In most early sett…