Search Results for author: Haiming Jin

Found 7 papers, 3 papers with code

CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting

1 code implementation4 Jun 2024 Jianrong Ding, Zhanyu Liu, Guanjie Zheng, Haiming Jin, Linghe Kong

To mitigate this gap, we theoretically analyze the optimization objective of dataset condensation for TS-forecasting and propose a new one-line plugin of dataset condensation designated as Dataset Condensation for Time Series Forecasting (CondTSF) based on our analysis.

Dataset Condensation Time Series +1

Prediction with Incomplete Data under Agnostic Mask Distribution Shift

no code implementations18 May 2023 Yichen Zhu, Jian Yuan, Bo Jiang, Tao Lin, Haiming Jin, Xinbing Wang, Chenghu Zhou

We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i. e., mask distribution may shift agnostically between training and testing.

User-Oriented Robust Reinforcement Learning

no code implementations15 Feb 2022 Haoyi You, Beichen Yu, Haiming Jin, Zhaoxing Yang, Jiahui Sun

Specifically, we define a new User-Oriented Robustness (UOR) metric for RL, which allocates different weights to the environments according to user preference and generalizes the max-min robustness metric.

reinforcement-learning Reinforcement Learning (RL)

DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning

4 code implementations10 Nov 2021 Zhaoxing Yang, Rong Ding, Haiming Jin, Yifei Wei, Haoyi You, Guiyun Fan, Xiaoying Gan, Xinbing Wang

In addition, with such modularization, the training algorithm of DeCOM separates the original constrained optimization into an unconstrained optimization on reward and a constraints satisfaction problem on costs.

Multi-agent Reinforcement Learning reinforcement-learning +1

Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems

no code implementations4 Sep 2020 Chang Liu, Jiahui Sun, Haiming Jin, Meng Ai, Qun Li, Cheng Zhang, Kehua Sheng, Guobin Wu, XiaoHu Qie, Xinbing Wang

Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

1 code implementation13 May 2019 Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Wei-Nan Zhang, Yong Yu, Haiming Jin, Zhenhui Li

The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.

Multi-agent Reinforcement Learning reinforcement-learning +1

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