Bo Dai

Address

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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 Embodied 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

• bodai at cc.gatech.edu    (for general academic work)
• bodai at google.com    (for Google related work)

daibond_alpha

Mar 2023 - NOW

Sep 2018 - NOW

Aug 2013 - Aug 2018

• Assistant Professor, School of Computational Science & Engineering, Georgia Tech

• Staff Research Scientist & Research Manager, Google Brain

• Ph.D., School of Computational Science & Engineering, Georgia Tech

Peer Reviewed Publications

• 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 ]

Current Students

Haotian Sun, Georgia Tech PhD

Yitong Li, Georgia Tech Master

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