14 citations · 39 across the 19 of their papers we have counts for
19 papers
Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit
Weizhi Nie, Yuhe Yu, Chen Zhang +3
In recent years, medical information technology has made it possible for electronic health record (EHR) to store fairly complete clinical data. This has brought health care into th…
Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations
Minshuo Chen, Jie Meng, Yu Bai +3
In real-world reinforcement learning (RL) systems, various forms of {\it impaired observability} can complicate matters. These situations arise when an agent is unable to observe t…
Chest X-ray Image Classification: A Causal Perspective
Weizhi Nie, Chen Zhang, Dan Song +4
The chest X-ray (CXR) is one of the most common and easy-to-get medical tests used to diagnose common diseases of the chest. Recently, many deep learning-based methods have been pr…
LightCurve MoE: A Dynamic Sparse Routing Mixture-of-Experts Architecture for Efficient Stellar Light Curve Classification
Cunshi Wang, Yu Bai, Xinrui Song +6
The classification of stellar light curves has become a key task in modern time-domain astronomy, fueled by the rapid growth of data from large-scale surveys such as Kepler and TES…
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
Yuanhao Wang, Qinghua Liu, Yu Bai +1
A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the curse of multiagency, where the description length of the game as well as the complexity of many existing lea…
Offline Learning in Markov Games with General Function Approximation
Yuheng Zhang, Yu Bai, Nan Jiang
We study offline multi-agent reinforcement learning (RL) in Markov games, where the goal is to learn an approximate equilibrium -- such as Nash equilibrium and (Coarse) Correlated…
PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs
Chunyang Wang, Desen Sun, Yuebin Bai
Dynamic Graph Neural Networks (DGNNs) have been broadly applied in various real-life applications, such as link prediction and pandemic forecast, to capture both static structural…
Conformal Predictor for Improving Zero-shot Text Classification Efficiency
Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu +2
Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification. 0shot models based on natural language inference (NLI) and next sentence pre…
Learning Rationalizable Equilibria in Multiplayer Games
Yuanhao Wang, Dingwen Kong, Yu Bai +1
A natural goal in multiagent learning besides finding equilibria is to learn rationalizable behavior, where players learn to avoid iteratively dominated actions. However, even in t…
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
Chen Gong, Zhou Yang, Yunpeng Bai +8
Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL…
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Dapeng Li, Bin Zhang +3
Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments. However, due…
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network
Bin Zhang, Yunpeng Bai, Zhiwei Xu +2
The application of deep reinforcement learning in multi-agent systems introduces extra challenges. In a scenario with numerous agents, one of the most important concerns currently…
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning
Michael Curry, Alexander Trott, Soham Phade +2
Real economies can be modeled as a sequential imperfect-information game with many heterogeneous agents, such as consumers, firms, and governments. Dynamic general equilibrium (DGE…
Near-Optimal Learning of Extensive-Form Games with Imperfect Information
Yu Bai, Chi Jin, Song Mei +1
This paper resolves the open question of designing near-optimal algorithms for learning imperfect-information extensive-form games from bandit feedback. We present the first line o…
Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution
Yunpeng Bai, Chen Gong, Bin Zhang +3
Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an imp…
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Yunpeng Bai, Dapeng Li +2
As one of the solutions to the decentralized partially observable Markov decision process (Dec-POMDP) problems, the value decomposition method has achieved significant results rece…
CMS-LSTM: Context Embedding and Multi-Scale Spatiotemporal Expression LSTM for Predictive Learning
Zenghao Chai, Zhengzhuo Xu, Yunpeng Bai +2
Spatiotemporal predictive learning (ST-PL) is a hotspot with numerous applications, such as object movement and meteorological prediction. It aims at predicting the subsequent fram…
Near-Optimal Reinforcement Learning with Self-Play
Yu Bai, Chi Jin, Tiancheng Yu
This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn the optimal p…
Directed-Weighting Group Lasso for Eltwise Blocked CNN Pruning
Ke Zhan, Shimiao Jiang, Yu Bai +3
Eltwise layer is a commonly used structure in the multi-branch deep learning network. In a filter-wise pruning procedure, due to the specific operation of the eltwise layer, all it…