22 citations · 34 across the 25 of their papers we have counts for
4 papers · 1 filter
Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability
Yafei Wang, Bo Pan, Wei Tu +6
Sample average approximation (SAA), a popular method for tractably solving stochastic optimization problems, enjoys strong asymptotic performance guarantees in settings with indepe…
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving
Lei Ding, Dengdeng Yu, Jinhan Xie +7
With widening deployments of natural language processing (NLP) in daily life, inherited social biases from NLP models have become more severe and problematic. Previous studies have…
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Ke Sun, Yafei Wang, Yi Liu +5
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its…
Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
Ke Sun, Yingnan Zhao, Enze Shi +4
The remarkable empirical performance of distributional reinforcement learning (RL) has garnered increasing attention to understanding its theoretical advantages over classical RL.…