44 citations · 113 across the 19 of their papers we have counts for
19 papers
OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
Sheng Yue, Xingyuan Hua, Ju Ren +3
In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal enviro…
How to Leverage Diverse Demonstrations in Offline Imitation Learning
Sheng Yue, Jiani Liu, Xingyuan Hua +4
Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental probl…
Kernelized Offline Contextual Dueling Bandits
Viraj Mehta, Ojash Neopane, Vikramjeet Das +3
Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning…
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback
Hang Wang, Sen Lin, Junshan Zhang
The ensemble method is a promising way to mitigate the overestimation issue in Q-learning, where multiple function approximators are used to estimate the action values. It is known…
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
Hang Wang, Sen Lin, Junshan Zhang
Warm-Start reinforcement learning (RL), aided by a prior policy obtained from offline training, is emerging as a promising RL approach for practical applications. Recent empirical…
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes
Peizhong Ju, Sen Lin, Mark S. Squillante +2
Transfer learning is a useful technique for achieving improved performance and reducing training costs by leveraging the knowledge gained from source tasks and applying it to targe…
Efficient Self-supervised Continual Learning with Progressive Task-correlated Layer Freezing
Li Yang, Sen Lin, Fan Zhang +2
Inspired by the success of Self-supervised learning (SSL) in learning visual representations from unlabeled data, a few recent works have studied SSL in the context of continual le…
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning
Sheng Yue, Guanbo Wang, Wei Shao +4
This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to exp…
Theory on Forgetting and Generalization of Continual Learning
Sen Lin, Peizhong Ju, Yingbin Liang +1
Continual learning (CL), which aims to learn a sequence of tasks, has attracted significant recent attention. However, most work has focused on the experimental performance of CL,…
Algorithm Design for Online Meta-Learning with Task Boundary Detection
Daouda Sow, Sen Lin, Yingbin Liang +1
Online meta-learning has recently emerged as a marriage between batch meta-learning and online learning, for achieving the capability of quick adaptation on new tasks in a lifelong…
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer
Sen Lin, Li Yang, Deliang Fan +1
By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward k…
TRGP: Trust Region Gradient Projection for Continual Learning
Sen Lin, Li Yang, Deliang Fan +1
Catastrophic forgetting is one of the major challenges in continual learning. To address this issue, some existing methods put restrictive constraints on the optimization space of…
Model-Based Offline Meta-Reinforcement Learning with Regularization
Sen Lin, Jialin Wan, Tengyu Xu +2
Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy. Offline…
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence
Mehmet Dedeoglu, Sen Lin, Zhaofeng Zhang +1
Learning generative models is challenging for a network edge node with limited data and computing power. Since tasks in similar environments share model similarity, it is plausible…
Distributed Q-Learning with State Tracking for Multi-agent Networked Control
Hang Wang, Sen Lin, Hamid Jafarkhani +1
This paper studies distributed Q-learning for Linear Quadratic Regulator (LQR) in a multi-agent network. The existing results often assume that agents can observe the global system…
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication
Sen Lin, Mehmet Dedeoglu, Junshan Zhang
Online meta-learning is emerging as an enabling technique for achieving edge intelligence in the IoT ecosystem. Nevertheless, to learn a good meta-model for within-task fast adapta…
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning
Sheng Yue, Ju Ren, Jiang Xin +2
In order to meet the requirements for performance, safety, and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge. Howeve…
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning
Sen Lin, Li Yang, Zhezhi He +2
While deep learning has achieved phenomenal successes in many AI applications, its enormous model size and intensive computation requirements pose a formidable challenge to the dep…
System Identification via Meta-Learning in Linear Time-Varying Environments
Sen Lin, Hang Wang, Junshan Zhang
System identification is a fundamental problem in reinforcement learning, control theory and signal processing, and the non-asymptotic analysis of the corresponding sample complexi…