Bo Dai

Address

Contact

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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 Agent AI upon Generative Models and Representation, 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, Google Brain

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

Peer Reviewed Publications

• Efficient Online Reinforcement Learning for Diffusion Policy
  Haitong Ma, Tianyi Chen, Kai Wang, Na Li, Bo Dai
  International Conference on Machine Learning (ICML) 2025.
  [ Paper ]

• Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF
  Shicong Cen, Jincheng Mei, Katayoon Goshvadi, Hanjun Dai, Tong Yang, Sherry Yang, Dale Schuurmans, Yuejie Chi, Bo Dai
  International Conference on Learning Representations (ICLR) 2025.
  [ Paper ]

• Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment
  Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen, Dale Schuurmans, Yuejie Chi, Bo Dai
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2025.
  [ Paper ]

• Scalable Spectral Representations for Multi-Agent Reinforcement Learning in Network MDPs
  Zhaolin Ren, Runyu Zhang, Bo Dai, Na Li
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2025.
  [ Paper ]

• Primal-Dual Spectral Representation for Off-policy Evaluation
  Yang Hu, Tianyi Chen, Kai Wang, Na Li, Bo Dai
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2025.
  [ Paper ]

• Diffusion Spectral Representation for Reinforcement Learning
  Dmitry Shribak, Chen-Xiao Gao, Yitong Li, Chenjun Xiao, Bo Dai
  Advances in Neural Information Processing Systems (NeurIPS) 2024.
  [ Paper ] [ Website ] [ Code ]

• 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 ]

Current Students

Haotian Sun, Georgia Tech CSE PhD

Changhao Li, Georgia Tech CSE PhD

Rushi Qiang, Georgia Tech CSE PhD

Tianyi Chen, Georgia Tech CSE PhD

Yitong Li, Georgia Tech CS Master

Aaron Trinh, Georgia Tech Undergraduate

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