Lantao Yu

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Weinan ZhangProfessor, Shanghai Jiao Tong UniversityDirección de correo verificada de sjtu.edu.cn
Stefano ErmonStanford UniversityDirección de correo verificada de cs.stanford.edu
Jun WangProfessor, Computer Science, University College LondonDirección de correo verificada de cs.ucl.ac.uk
Tianhe YuGoogle DeepMindDirección de correo verificada de google.com
Jiaming SongStanford UniversityDirección de correo verificada de cs.stanford.edu
Yong Yu (俞勇)Professor, Shanghai Jiao Tong UniversityDirección de correo verificada de sjtu.edu.cn
Chelsea FinnStanford University, Physical IntelligenceDirección de correo verificada de cs.stanford.edu
Dell ZhangInstitute of Artificial Intelligence (TeleAI), China TelecomDirección de correo verificada de ieee.org
Sergey LevineUC Berkeley, Physical IntelligenceDirección de correo verificada de eecs.berkeley.edu
Tengyu MAStanford UniversityDirección de correo verificada de stanford.edu
Garrett ThomasStanford UniversityDirección de correo verificada de stanford.edu
James ZouStanford UniversityDirección de correo verificada de stanford.edu
Yang SongOpenAIDirección de correo verificada de openai.com
Chence ShiQuebec AI Institute (Mila)Dirección de correo verificada de umontreal.ca
Jian Tang (唐建）Associate Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI ChairDirección de correo verificada de hec.ca
Fei FangCarnegie Mellon UniversityDirección de correo verificada de cmu.edu
Willie NeiswangerAssistant Professor of Computer Science, University of Southern CaliforniaDirección de correo verificada de cs.stanford.edu
Yi WuInstitute for Interdisciplinary Information Sciences, Tsinghua UniversityDirección de correo verificada de mail.tsinghua.edu.cn
Chenlin MengStanford UniversityDirección de correo verificada de stanford.edu
Yiwei BaiCornell UniversityDirección de correo verificada de cornell.edu

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Lantao Yu

Ph.D., Computer Science, Stanford University

Dirección de correo verificada de cs.stanford.edu

Machine Learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Seqgan: sequence generative adversarial nets with policy gradient L Yu, W Zhang, J Wang, Y Yu The Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017), 2017	3041	2017
MOPO: Model-based Offline Policy Optimization T Yu, G Thomas, L Yu, S Ermon, J Zou, S Levine, C Finn, T Ma 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020	942	2020
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models J Wang, L Yu, W Zhang, Y Gong, Y Xu, B Wang, P Zhang, D Zhang SIGIR 2017 (Best Paper Award Honorable Mention), 2017	740	2017
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang International Conference on Learning Representations (ICLR 2022), 2022	591	2022
A Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration X Wang, L Yu, K Ren, G Tao, W Zhang, Y Yu, J Wang 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), 2017	183	2017
Multi-Agent Adversarial Inverse Reinforcement Learning L Yu, J Song, S Ermon 36th International Conference on Machine Learning (ICML 2019), 2019	171	2019
Lipschitz Generative Adversarial Nets Z Zhou, J Liang, Y Song, L Yu, H Wang, W Zhang, Y Yu, Z Zhang 36th International Conference on Machine Learning (ICML 2019), 2019	106	2019
Deep Reinforcement Learning for Green Security Games with Real-Time Information Y Wang, ZR Shi, L Yu, Y Wu, R Singh, L Joppa, F Fang The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), 2018	98	2018
Understanding self-supervised learning with dual deep networks Y Tian, L Yu, X Chen, S Ganguli arXiv preprint arXiv:2010.00578, 2020	94	2020
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables L Yu, T Yu, C Finn, S Ermon 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019	89	2019
Training Deep Energy-Based Models with f-Divergence Minimization L Yu, Y Song, J Song, S Ermon 37th International Conference on Machine Learning (ICML 2020), 2020	50	2020
A Study of AI Population Dynamics with Million-agent Reinforcement Learning Y Yang, L Yu, Y Bai*, Y Wen, W Zhang, J Wang Proceedings of the 17th International Conference on Autonomous Agents and …, 2018	46*	2018
Cot: Cooperative training for generative modeling of discrete data S Lu, L Yu, S Feng, Y Zhu, W Zhang International Conference on Machine Learning, 4164-4172, 2019	44*	2019
Adversarial Inverse Reinforcement Learning with Self-attention Dynamics Model J Sun, L Yu, P Dong, B Lu, B Zhou IEEE Robotics and Automation Letters. (RA-L), 2021	38	2021
Exploiting Data and Human Knowledge for Predicting Wildlife Poaching S Gurumurthy, L Yu, C Zhang, Y Jin, W Li, H Zhang, F Fang ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS 2018), 2018	23	2018
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets Z Zhou, Y Song, L Yu, H Wang, J Liang, W Zhang, Z Zhang, Y Yu arXiv preprint arXiv:1807.00751, 2018	21*	2018
A General Recipe for Likelihood-free Bayesian Optimization J Song, L Yu, W Neiswanger, S Ermon The 39th International Conference on Machine Learning (ICML 2022), 2022	19	2022
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck Y Song, L Yu, Z Cao, Z Zhou, J Shen, S Shao, W Zhang, Y Yu The 24th European Conference on Artificial Intelligence (ECAI 2020), 2019	18	2019
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip Y Song, M Xu, L Yu, H Zhou, S Shao, Y Yu The 34th AAAI Conference on Artificial Intelligence (AAAI 2020), 2019	17	2019
Offline imitation learning with suboptimal demonstrations via relaxed distribution matching L Yu, T Yu, J Song, W Neiswanger, S Ermon Proceedings of the AAAI conference on artificial intelligence 37 (9), 11016 …, 2023	16	2023

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