62 citations · 84 across the 12 of their papers we have counts for
10 papers · 1 filter
SOLAR: Communication-Efficient Model Adaptation via Subspace-Oriented Latent Adapter Reparametrization
Seyed Mahmoud Sajjadi Mohammadabadi, Xiaolong Ma, Lei Yang +2
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, enable scalable adaptation of foundation models by injecting low-rank adapters. However, their communication and stora…
AugFL: Augmenting Federated Learning with Pretrained Models
Sheng Yue, Zerui Qin, Yongheng Deng +3
Federated Learning (FL) has garnered widespread interest in recent years. However, owing to strict privacy policies or limited storage capacities of training participants such as I…
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…
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…
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…
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…
Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention
Wei Shao, Zhiling Jin, Shuo Wang +6
Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LS…
Robust Event Classification Using Imperfect Real-world PMU Data
Yunchuan Liu, Lei Yang, Amir Ghasemkhani +4
This paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to dir…