16 citations · 24 across the 8 of their papers we have counts for
8 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…
Order Constraints in Optimal Transport
Fabian Lim, Laura Wynter, Shiau Hong Lim
Optimal transport is a framework for comparing measures whereby a cost is incurred for transporting one measure to another. Recent works have aimed to improve optimal transport pla…
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…
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization
Achintya Kundu, Pengqian Yu, Laura Wynter +1
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of me…
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…