30 citations · 88 across the 30 of their papers we have counts for
14 papers · 1 filter
Learning How to Propagate Messages in Graph Neural Networks
Teng Xiao, Zhengyu Chen, Donglin Wang +1
This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the…
A General Offline Reinforcement Learning Framework for Interactive Recommendation
Teng Xiao, Donglin Wang
This paper studies the problem of learning interactive recommender systems from logged feedbacks without any exploration in online environments. We address the problem by proposing…
Learning on Graphs with Out-of-Distribution Nodes
Yu Song, Donglin Wang
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs. While existing GNNs have shown great performance on various tasks related to gra…
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Yachen Kang, Li He, Jinxin Liu +2
Preference-based reinforcement learning (PbRL) promises to learn a complex reward function with binary human preference. However, such human-in-the-loop formulation requires consid…
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
Jinxin Liu, Ziqi Zhang, Zhenyu Wei +4
Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data. Although avoiding the time-consuming online interactions in RL, it poses challen…
Beyond Reward: Offline Preference-guided Policy Optimization
Yachen Kang, Diyuan Shi, Jinxin Liu +2
This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online…
CEIL: Generalized Contextual Imitation Learning
Jinxin Liu, Li He, Yachen Kang +3
In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired…
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Jinxin Liu, Hongyin Zhang, Zifeng Zhuang +3
In this work, we decouple the iterative bi-level offline RL (value estimation and policy extraction) from the offline training phase, forming a non-iterative bi-level paradigm and…
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
Jinxin Liu, Lipeng Zu, Li He +1
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. Howev…
OER: Offline Experience Replay for Continual Offline Reinforcement Learning
Sibo Gai, Donglin Wang, Li He
The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline t…
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning
Min Zhang, Zifeng Zhuang, Zhitao Wang +2
Gradient-based meta-learning (GBML) algorithms are able to fast adapt to new tasks by transferring the learned meta-knowledge, while assuming that all tasks come from the same dist…
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
Siteng Huang, Biao Gong, Yutong Feng +3
Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relyi…
Behavior Proximal Policy Optimization
Zifeng Zhuang, Kun Lei, Jinxin Liu +2
Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-ac…
Graph based Environment Representation for Vision-and-Language Navigation in Continuous Environments
Ting Wang, Zongkai Wu, Feiyu Yao +1
Vision-and-Language Navigation in Continuous Environments (VLN-CE) is a navigation task that requires an agent to follow a language instruction in a realistic environment. The unde…