Address
Contact
I am an assistant professor at CSE in Georgia Tech. I am also affiliated with Google DeepMind as a staff research scientist.
My principal research interest lies on Agent AI upon Generative Models, aiming for creating agents with decision-making and planning ability through modeling the world.
CODA E1342A, 756 W Peachtree St NW, Atlanta, GA 30308
Mar 2023 - NOW
Sep 2018 - NOW
Aug 2013 - Aug 2018
Assistant Professor, School of Computational Science & Engineering, Georgia Tech
Staff Research Scientist, Google Brain
Ph.D., School of Computational Science & Engineering, Georgia Tech
Peer Reviewed Publications
Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint
Haitong Ma, Zhaolin Ren, Bo Dai, Na Li
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024.
Oral
[ Paper ]
[ Website ]
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai
International Conference on Machine Learning (ICML) 2024.
Spotlight
[ Paper ]
[ Website ]
[ Code ]
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai
International Conference on Machine Learning (ICML) 2024.
[ Paper ]
[ Website ]
[ Code ]
Ordering-based Conditions for Global Convergence of Policy Gradient Methods
Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvari, Dale Schuurmans
Advances in Neural Information Processing Systems (NeurIPS) 2023.
Oral
[ Paper ]
Learning Universal Policies via Text-Guided Video Generation
Yilun Du*, Mengjiao Yang*, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, Pieter Abbeel
Advances in Neural Information Processing Systems (NeurIPS) 2023.
Spotlight
[ Paper ][ Blog ][ Website ]
AdaPlanner: Adaptive Planning from Feedback with Language Models
Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang
Advances in Neural Information Processing Systems (NeurIPS) 2023.
[ Paper ][ Code ]
Stochastic Gradient Succeeds for Bandits
Jincheng Mei*, Zixin Zhong*, Bo Dai, Alekh Agarwal, Csaba Szepesvari, Dale Schuurmans
International Conference on Machine Learning (ICML) 2023.
[ Paper ]
Learning to Optimize with Stochastic Dominance Constraints
Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai
International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
[ Paper ]
Latent Variable Representation for Reinforcement Learning
Tongzheng Ren, Chenjun Xiao, Tianjun Zhang,
Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai
International Conference on Learning Representations (ICLR) 2023.
[ Paper ][Website][ Code ]
Discrete Langevin Samplers via Wasserstein Gradient Flow
Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans
International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
[ Paper ]
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning
Tongzheng Ren, Tianjun Zhang, Csaba Szepesvari, Bo Dai
Conference on Uncertainty in Artificial Intelligence (UAI) 2022.
[ Paper ]
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
Haoming Jiang, Bo Dai, Mengjiao Yang, Tuo Zhao and Wei Wei
Conference on Empirical Methods in Natural Language Processing (EMNLP) 2021.
[ Paper ]
[ Code ]
CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai*, Ofir Nachum*, Yinlam Chow, Lihong Li, Csaba Szepesvari, Dale Schuurmans
Advances in Neural Information Processing Systems (NeurIPS) 2020.
Spotlight
[ Paper ]
[ Code ]
Differentiable Top-k Operator with Optimal Transport
Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister
Advances in Neural Information Processing Systems (NeurIPS) 2020.
[ Paper ]
[ Code ]
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Ofir Nachum*, Yinlam Chow*, Bo Dai, Lihong Li
Advances in Neural Information Processing Systems (NeurIPS) 2019.
Spotlight
[ Paper ]
[ Code ]
Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai*, Zhen Liu*, Hanjun Dai*, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
Advances in Neural Information Processing Systems (NeurIPS) 2019.
[ Paper ]
[ Code ]
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
International Conference on Machine Learning (ICML) 2018.
Long Oral
[ Paper ]
Syntax-Directed Variational Autoencoder for Structured Data.
Hanjun Dai*, Yingtao Tian*, Bo Dai, Steven Skiena and Le Song
International Conference on Learning Representations (ICLR) 2018
[ arxiv ] [ Code ]
Learning from Conditional Distributions via Dual Embeddings
Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song
International Conference on Artificial Intelligence and Statistics (AISTATS) 2017.
[ Paper ]
Discriminateive Embeddings of Latent Variable Models for Structured Data.
Hanjun Dai, Bo Dai and Le Song
International Conference on Machine Learning (ICML) 2016.
[ arxiv ] [ Code ]
Provable Bayesian Inference via Particle Mirror Descent.
Bo Dai, Niao He, Hanjun Dai and Le Song
International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
Best Paper
[ Paper ]
PhD Thesis
Learning over Functions, Distributions and Dynamics via Stochastic Optimization.
Bo Dai, 2018
[ Thesis ]
CSE 6243, Advanced Machine Learning, Fall 2024, Georgia Tech
CSE 6243, Advanced Machine Learning, Fall 2023, Georgia Tech
Reinforcement Learning via Optimization Lens, Summer 2021, ETH & EPFL
Reinforcement Learning via Optimization Lens, Fall 2020, Google Brainiversity
CSE 6740, Machine Learning, Spring 2015/Fall 2016, Georgia Tech
AISTATS Best Paper Award, 2016
NeurIPS Machine Learning for Molecules and Materials workshop Best Paper Award, 2017
Ross Fellowship, 2011-2012
Summa Cum Laude, Nanjing University
National Scholarship, Nanjing University
Current Students
Haotian Sun, Georgia Tech CSE PhD
Yitong Li, Georgia Tech CS Master
Dmitry Shribak, Georgia Tech ECE PhD
Former Members
Jincheng Mei, UAlberta PhD, Research Scientist at Google Brain
Chenjun Xiao, UAlberta PhD, Assistant Professor at CUHK, Shenzhen
Zhuangdi Zhu, MSU PhD, Assistant Professor at George Mason University
Tongzheng Ren, UT Austin PhD, Quantitative Researcher at Citadel Securities
Action Editor of Transactions on Machine Learning Research (TMLR)
(Senior) Area Chair of top conferences for multiple years between 2019-2024: NeurIPS, ICML, ICLR, AISTATS
Co-organizer of Workshop on Evaluations and Assessments of Neural Conversation Systems at EMNLP 2021
Co-organizer of Workshop on Reinforcement Learning at Google, 2021
Co-organizer of Optimization Foundations of Reinforcement Learning Workshop at NeurIPS 2019