research
∙
12/10/2022
Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees
We revisit the domain of off-policy policy optimization in RL from the p...
share
research
∙
12/06/2022
Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots
Many real-world continuous control problems are in the dilemma of weighi...
research
∙
09/27/2022
Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265
This paper presents a reinforcement learning (RL) framework that utilize...
research
∙
03/08/2022
Neural Contextual Bandits via Reward-Biased Maximum Likelihood Estimation
Reward-biased maximum likelihood estimation (RBMLE) is a classic princip...
research
∙
10/26/2021
Hinge Policy Optimization: Rethinking Policy Improvement and Reinterpreting PPO
Policy optimization is a fundamental principle for designing reinforceme...
research
∙
10/05/2021
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL
Whittle index policy is a powerful tool to obtain asymptotically optimal...
research
∙
06/08/2021
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
Bayesian optimization (BO) conventionally relies on handcrafted acquisit...
research
∙
02/22/2021
Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization
Action-constrained reinforcement learning (RL) is a widely-used approach...
research
∙
10/08/2020
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits
Modifying the reward-biased maximum likelihood method originally propose...
research
∙
01/27/2020
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning
With the explosive growth of online products and content, recommendation...
research
∙
11/03/2019
Fresher Content or Smoother Playback? A Brownian-Approximation Framework for Scheduling Real-Time Wireless Video Streams
This paper presents a Brownian-approximation framework to optimize the q...
research
∙
11/03/2019
Fresher Content or Smoother Playback? A Brownian-Approximation Framework for Real-Time Video Delivery in Wireless Networks
This paper presents a Brownian-approximation framework to optimize the q...
research
∙
07/02/2019
Bandit Learning Through Biased Maximum Likelihood Estimation
We propose BMLE, a new family of bandit algorithms, that are formulated ...
research
∙
11/14/2018
Streaming Network Embedding through Local Actions
Recently, considerable research attention has been paid to network embed...
research
∙
10/29/2018
Heteroscedastic Bandits with Reneging
Although shown to be useful in many areas as models for solving sequenti...
research
∙
01/13/2017
