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Lantao Yu
Lantao Yu
Ph.D., Computer Science, Stanford University
Verified email at cs.stanford.edu
Title
Cited by
Cited by
Year
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
27982017
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
7522020
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
6872017
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
4062022
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
1822017
Multi-Agent Adversarial Inverse Reinforcement Learning
L Yu, J Song, S Ermon
36th International Conference on Machine Learning (ICML 2019), 2019
1362019
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
992019
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
872018
Understanding self-supervised learning with dual deep networks
Y Tian, L Yu, X Chen, S Ganguli
arXiv preprint arXiv:2010.00578, 2020
802020
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
772019
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
482020
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
45*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
39*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
212021
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
20*2018
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
202018
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
172019
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
142022
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
142019
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
112023
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