102 citations · 255 across the 30 of their papers we have counts for
30 papers
Invariant Learning via Probability of Sufficient and Necessary Causes
Mengyue Yang, Zhen Fang, Yonggang Zhang +5
Out-of-distribution (OOD) generalization is indispensable for learning models in the wild, where testing distribution typically unknown and different from the training. Recent meth…
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Jiarui Jin, Xianyu Chen, Weinan Zhang +5
Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existi…
Pre-training Code Representation with Semantic Flow Graph for Effective Bug Localization
Yali Du, Zhongxing Yu
Enlightened by the big success of pre-training in natural language processing, pre-trained models for programming languages have been widely used to promote code intelligence in re…
ChessGPT: Bridging Policy Learning and Language Modeling
Xidong Feng, Yicheng Luo, Ziyan Wang +6
When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment,…
PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation
Runze Liu, Yali Du, Fengshuo Bai +2
In preference-based Reinforcement Learning (RL), obtaining a large number of preference labels are both time-consuming and costly. Furthermore, the queried human preferences cannot…
Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination
Yang Li, Shao Zhang, Jichen Sun +5
Securing coordination between AI agent and teammates (human players or AI agents) in contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot Coo…
Introspective Tips: Large Language Model for In-Context Decision Making
Liting Chen, Lu Wang, Hang Dong +9
The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks. In this study, we em…
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Yudi Zhang, Yali Du, Biwei Huang +4
A major challenge in reinforcement learning is to determine which state-action pairs are responsible for future rewards that are delayed. Reward redistribution serves as a solution…
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
Sirui Chen, Zhaowei Zhang, Yaodong Yang +1
Centralized Training with Decentralized Execution (CTDE) has been proven to be an effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the major chal…
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
Shangding Gu, Alap Kshirsagar, Yali Du +3
Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL)…
Cooperative Open-ended Learning Framework for Zero-shot Coordination
Yang Li, Shao Zhang, Jichen Sun +4
Zero-shot coordination in cooperative artificial intelligence (AI) remains a significant challenge, which means effectively coordinating with a wide range of unseen partners. Previ…
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
Lukas Schäfer, Oliver Slumbers, Stephen McAleer +3
Multi-agent reinforcement learning (MARL) requires agents to explore within a vast joint action space to find joint actions that lead to coordination. Existing value-based MARL alg…
PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination
Xingzhou Lou, Jiaxian Guo, Junge Zhang +3
Zero-shot human-AI coordination holds the promise of collaborating with humans without human data. Prevailing methods try to train the ego agent with a population of partners via s…
Multi-queue Momentum Contrast for Microvideo-Product Retrieval
Yali Du, Yinwei Wei, Wei Ji +3
The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their…
Contextual Transformer for Offline Meta Reinforcement Learning
Runji Lin, Ye Li, Xidong Feng +6
The pretrain-finetuning paradigm in large-scale sequence models has made significant progress in natural language processing and computer vision tasks. However, such a paradigm is…
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection
Yang Yang, Hongjian Sun, Jialei Gong +1
Nonlinear dimensionality reduction lacks interpretability due to the absence of source features in low-dimensional embedding space. We propose an interpretable method featMAP to pr…
Scalable Model-based Policy Optimization for Decentralized Networked Systems
Yali Du, Chengdong Ma, Yuchen Liu +4
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks. Such a challenge is more outstanding in m…
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
Shangding Gu, Long Yang, Yali Du +4
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world application…
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Yunqiu Xu, Meng Fang, Ling Chen +3
Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, th…
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL
Rui Yang, Yiming Lu, Wenzhe Li +6
Solving goal-conditioned tasks with sparse rewards using self-supervised learning is promising because of its simplicity and stability over current reinforcement learning (RL) algo…
Learning to Identify Top Elo Ratings: A Dueling Bandits Approach
Xue Yan, Yali Du, Binxin Ru +3
The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players. Recently it has been also integrated into machine learning algorithms in evaluati…
GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning
Jingqing Ruan, Yali Du, Xuantang Xiong +6
Many real-world scenarios involve a team of agents that have to coordinate their policies to achieve a shared goal. Previous studies mainly focus on decentralized control to maximi…
Generalization in Text-based Games via Hierarchical Reinforcement Learning
Yunqiu Xu, Meng Fang, Ling Chen +2
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the general…
Is Nash Equilibrium Approximator Learnable?
Zhijian Duan, Wenhan Huang, Dinghuai Zhang +4
In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated from a distribution. First, we offer a gener…