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Li Zhao 0007
Person information
- affiliation: Microsoft Research Asia, Beijing, China
- affiliation (PhD 2016): Tsinghua University, Beijing, China
Other persons with the same name
- Li Zhao — disambiguation page
- Li Zhao 0001 — Children's National Medical Center, Washington, DC, USA (and 2 more)
- Li Zhao 0002 — Intel Corporation, Hillsboro, OR, USA (and 1 more)
- Li Zhao 0003 — Southeast University, School of Information Science and Engineering, MOE Key Laboratory of Underwater Acoustic Signal Processing, Nanjing, China (and 1 more)
- Li Zhao 0004 — Western Sydney University, School of Business, Penrith, NSW, Australia
- Li Zhao 0005 — Wenzhou University, Zhejiang, China
- Li Zhao 0006 — Tsinghua University, Department of Computer Science and Technology, China
- Li Zhao 0008 — Fudan University, Shanghai Institute for Advanced Communication and Data Science, Shanghai, China (and 1 more)
- Li Zhao 0009 — Jiangsu University of Science and Technology, China
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2020 – today
- 2024
- [c43]Yunseon Choi, Sangmin Bae, Seonghyun Ban, Minchan Jeong, Chuheng Zhang, Lei Song, Li Zhao, Jiang Bian, Kee-Eung Kim:
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL. ACL (1) 2024: 8252-8271 - [c42]Yunseon Choi, Li Zhao, Chuheng Zhang, Lei Song, Jiang Bian, Kee-Eung Kim:
Diversification of Adaptive Policy for Effective Offline Reinforcement Learning. IJCAI 2024: 3863-3871 - [c41]Qingpeng Cai, Xiangyu Zhao, Ling Pan, Xin Xin, Jin Huang, Weinan Zhang, Li Zhao, Dawei Yin, Grace Hui Yang:
AgentIR: 1st Workshop on Agent-based Information Retrieval. SIGIR 2024: 3025-3028 - [i26]Guangran Cheng, Chuheng Zhang, Wenzhe Cai, Li Zhao, Changyin Sun, Jiang Bian:
Empowering Large Language Models on Robotic Manipulation with Affordance Prompting. CoRR abs/2404.11027 (2024) - [i25]Han Zhong, Guhao Feng, Wei Xiong, Li Zhao, Di He, Jiang Bian, Liwei Wang:
DPO Meets PPO: Reinforced Token Optimization for RLHF. CoRR abs/2404.18922 (2024) - [i24]Wentao Zhang, Junliang Guo, Tianyu He, Li Zhao, Linli Xu, Jiang Bian:
Video In-context Learning. CoRR abs/2407.07356 (2024) - [i23]Yunseon Choi, Sangmin Bae, Seonghyun Ban, Minchan Jeong, Chuheng Zhang, Lei Song, Li Zhao, Jiang Bian, Kee-Eung Kim:
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL. CoRR abs/2407.14733 (2024) - 2023
- [c40]Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei Song, Jiang Bian:
Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem. AAAI 2023: 8132-8140 - [c39]Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, Jiang Bian:
H-TSP: Hierarchically Solving the Large-Scale Traveling Salesman Problem. AAAI 2023: 9345-9353 - [c38]Yuanying Cai, Chuheng Zhang, Hanye Zhao, Li Zhao, Jiang Bian:
Curriculum Offline Reinforcement Learning. AAMAS 2023: 1221-1229 - [c37]Xin Xin, Xiangyu Zhao, Jin Huang, Weinan Zhang, Li Zhao, Dawei Yin, Grace Hui Yang:
DRL4IR: 4th Workshop on Deep Reinforcement Learning for Information Retrieval. CIKM 2023: 5304-5307 - [c36]Jinpeng Zhang, Yufeng Zheng, Chuheng Zhang, Li Zhao, Lei Song, Yuan Zhou, Jiang Bian:
Robust Situational Reinforcement Learning in Face of Context Disturbances. ICML 2023: 41973-41989 - [c35]Chuheng Zhang, Yitong Duan, Xiaoyu Chen, Jianyu Chen, Jian Li, Li Zhao:
Towards Generalizable Reinforcement Learning for Trade Execution. IJCAI 2023: 4975-4983 - [c34]Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu:
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance. KDD 2023: 4003-4012 - [c33]Xin-Qiang Cai, Pushi Zhang, Li Zhao, Jiang Bian, Masashi Sugiyama, Ashley Llorens:
Distributional Pareto-Optimal Multi-Objective Reinforcement Learning. NeurIPS 2023 - [i22]Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, Jiang Bian:
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem. CoRR abs/2304.09395 (2023) - [i21]Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei Song, Jiang Bian:
Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem. CoRR abs/2304.09407 (2023) - [i20]Xiaoyu Chen, Shenao Zhang, Pushi Zhang, Li Zhao, Jianyu Chen:
Asking Before Action: Gather Information in Embodied Decision Making with Language Models. CoRR abs/2305.15695 (2023) - [i19]Xianliang Yang, Zhihao Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, Jiang Bian:
A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management. CoRR abs/2306.07542 (2023) - [i18]Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu:
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance. CoRR abs/2307.03119 (2023) - [i17]Chuheng Zhang, Yitong Duan, Xiaoyu Chen, Jianyu Chen, Jian Li, Li Zhao:
Towards Generalizable Reinforcement Learning for Trade Execution. CoRR abs/2307.11685 (2023) - [i16]Lei Song, Chuheng Zhang, Li Zhao, Jiang Bian:
Pre-Trained Large Language Models for Industrial Control. CoRR abs/2308.03028 (2023) - 2022
- [c32]Guoqing Liu, Mengzhang Cai, Li Zhao, Tao Qin, Adrian Brown, Jimmy Bischoff, Tie-Yan Liu:
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation. CoG 2022: 237-244 - [c31]Yuanying Cai, Chuheng Zhang, Li Zhao, Wei Shen, Xuyun Zhang, Lei Song, Jiang Bian, Tao Qin, Tieyan Liu:
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets. ICDM 2022: 21-30 - [c30]Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu:
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. ICLR 2022 - [c29]Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu:
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context. NeurIPS 2022 - [c28]Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu:
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. NeurIPS 2022 - [c27]Xiangyu Zhao, Xin Xin, Weinan Zhang, Li Zhao, Dawei Yin, Grace Hui Yang:
DRL4IR: 3rd Workshop on Deep Reinforcement Learning for Information Retrieval. SIGIR 2022: 3488-3491 - [i15]Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu:
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. CoRR abs/2202.06450 (2022) - [i14]Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu:
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. CoRR abs/2205.12418 (2022) - [i13]Guoqing Liu, Mengzhang Cai, Li Zhao, Tao Qin, Adrian Brown, Jimmy Bischoff, Tie-Yan Liu:
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation. CoRR abs/2207.08379 (2022) - [i12]Yuanying Cai, Chuheng Zhang, Li Zhao, Wei Shen, Xuyun Zhang, Lei Song, Jiang Bian, Tao Qin, Tieyan Liu:
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets. CoRR abs/2212.02125 (2022) - [i11]Yuandong Ding, Mingxiao Feng, Guozi Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, Houqiang Li, Yan Jin, Jiang Bian:
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management. CoRR abs/2212.07684 (2022) - [i10]Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu:
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context. CoRR abs/2212.12735 (2022) - 2021
- [j2]Guoqing Liu, Li Zhao, Pushi Zhang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu:
Demonstration actor critic. Neurocomputing 434: 194-202 (2021) - [c26]Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Jian Li, Nenghai Yu, Tie-Yan Liu:
Return-Based Contrastive Representation Learning for Reinforcement Learning. ICLR 2021 - [c25]Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minlie Huang, Tao Qin, Tie-Yan Liu:
Independence-aware Advantage Estimation. IJCAI 2021: 3349-3355 - [c24]Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu:
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions. NeurIPS 2021: 1519-1529 - [c23]Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu:
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning. NeurIPS 2021: 3029-3042 - [c22]Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu:
Curriculum Offline Imitating Learning. NeurIPS 2021: 6266-6277 - [c21]Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin, Grace Hui Yang:
DRL4IR: 2nd Workshop on Deep Reinforcement Learning for Information Retrieval. SIGIR 2021: 2681-2684 - [i9]Guoqing Liu, Chuheng Zhang, Li Zhao, Tao Qin, Jinhua Zhu, Jian Li, Nenghai Yu, Tie-Yan Liu:
Return-Based Contrastive Representation Learning for Reinforcement Learning. CoRR abs/2102.10960 (2021) - [i8]Pushi Zhang, Xiaoyu Chen, Li Zhao, Wei Xiong, Tao Qin, Tie-Yan Liu:
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions. CoRR abs/2110.13578 (2021) - [i7]Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu:
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning. CoRR abs/2110.14118 (2021) - [i6]Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu:
Curriculum Offline Imitation Learning. CoRR abs/2111.02056 (2021) - 2020
- [c20]Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu:
RD$^2$: Reward Decomposition with Representation Decomposition. NeurIPS 2020 - [c19]Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie:
Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs. SIGIR 2020: 239-248 - [c18]Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin, Grace Hui Yang, Alex Beutel:
Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances. SIGIR 2020: 2468-2471 - [i5]Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, Hsiao-Wuen Hon:
Suphx: Mastering Mahjong with Deep Reinforcement Learning. CoRR abs/2003.13590 (2020)
2010 – 2019
- 2019
- [j1]Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Enhong Chen, Tie-Yan Liu:
Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation. IEEE ACM Trans. Audio Speech Lang. Process. 27(10): 1564-1576 (2019) - [c17]Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu:
Trust Region Evolution Strategies. AAAI 2019: 4352-4359 - [c16]Zichuan Lin, Li Zhao, Jiang Bian, Tao Qin, Guangwen Yang:
Unified Policy Optimization for Robust Reinforcement Learning. ACML 2019: 395-410 - [c15]Zhige Li, Derek Yang, Li Zhao, Jiang Bian, Tao Qin, Tie-Yan Liu:
Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding. KDD 2019: 894-902 - [c14]Chi Chen, Li Zhao, Jiang Bian, Chunxiao Xing, Tie-Yan Liu:
Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction. KDD 2019: 2376-2384 - [c13]Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu:
Fully Parameterized Quantile Function for Distributional Reinforcement Learning. NeurIPS 2019: 6190-6199 - [c12]Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Tie-Yan Liu, Guangwen Yang:
Distributional Reward Decomposition for Reinforcement Learning. NeurIPS 2019: 6212-6221 - [i4]Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu:
Fully Parameterized Quantile Function for Distributional Reinforcement Learning. CoRR abs/1911.02140 (2019) - [i3]Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Guangwen Yang, Tie-Yan Liu:
Distributional Reward Decomposition for Reinforcement Learning. CoRR abs/1911.02166 (2019) - 2018
- [c11]Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Guiquan Liu, Tie-Yan Liu:
Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization. AAAI 2018: 5553-5560 - [c10]Lijun Wu, Fei Tian, Li Zhao, Jianhuang Lai, Tie-Yan Liu:
Word Attention for Sequence to Sequence Text Understanding. AAAI 2018: 5578-5585 - [c9]Jun Feng, Minlie Huang, Li Zhao, Yang Yang, Xiaoyan Zhu:
Reinforcement Learning for Relation Classification From Noisy Data. AAAI 2018: 5779-5786 - [c8]Tianyang Zhang, Minlie Huang, Li Zhao:
Learning Structured Representation for Text Classification via Reinforcement Learning. AAAI 2018: 6053-6060 - [c7]Lijun Wu, Yingce Xia, Fei Tian, Li Zhao, Tao Qin, Jianhuang Lai, Tie-Yan Liu:
Adversarial Neural Machine Translation. ACML 2018: 534-549 - [c6]Fei Gao, Lijun Wu, Li Zhao, Tao Qin, Xueqi Cheng, Tie-Yan Liu:
Efficient Sequence Learning with Group Recurrent Networks. NAACL-HLT 2018: 799-808 - [i2]Jun Feng, Minlie Huang, Li Zhao, Yang Yang, Xiaoyan Zhu:
Reinforcement Learning for Relation Classification from Noisy Data. CoRR abs/1808.08013 (2018) - 2017
- [c5]Lijun Wu, Li Zhao, Tao Qin, Jianhuang Lai, Tie-Yan Liu:
Sequence Prediction with Unlabeled Data by Reward Function Learning. IJCAI 2017: 3098-3104 - [i1]Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu:
Adversarial Neural Machine Translation. CoRR abs/1704.06933 (2017) - 2016
- [c4]Li Zhao, Minlie Huang, Ziyu Yao, Rongwei Su, Yingying Jiang, Xiaoyan Zhu:
Semi-Supervised Multinomial Naive Bayes for Text Classification by Leveraging Word-Level Statistical Constraint. AAAI 2016: 2877-2884 - [c3]Yequan Wang, Minlie Huang, Xiaoyan Zhu, Li Zhao:
Attention-based LSTM for Aspect-level Sentiment Classification. EMNLP 2016: 606-615 - 2015
- [c2]Li Zhao, Minlie Huang, Jiashen Sun, Hengliang Luo, Xiankai Yang, Xiaoyan Zhu:
Sentiment Extraction by Leveraging Aspect-Opinion Association Structure. CIKM 2015: 343-352 - 2014
- [c1]Li Zhao, Minlie Huang, Haiqiang Chen, Junjun Cheng, Xiaoyan Zhu:
Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency. EMNLP 2014: 1614-1623
Coauthor Index
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last updated on 2024-12-04 20:15 CET by the dblp team
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