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Bo Dai 0001
Person information
- affiliation: Google Brain, USA
- affiliation (PhD): Georgia Institute of Technology, Atlanta, GA, USA
- affiliation (former): Chinese Academy of Science, Institute of Automation, NLPR/LIAMA, Beijing, China
Other persons with the same name
- Bo Dai 0002
— Shanghai AI Laboratory, China (and 2 more)
- Bo Dai 0003 — University of Technology of Troyes, Charles Delaunay Institute, France
- Bo Dai 0004
- Bo Dai 0005
- Bo Dai 0006 — University of Electronic Science and Technology, School of Computer Science and Engineering, Chengdu, China
- Bo Dai 0007 — State Grid Zhejiang Electric Power Corporation Information and Telecommunication Branch, China
- Bo Dai 0008
- Bo Dai 0009 — Beijing Institute of Petrochemical Technology, College of Information Engineering, China
- Bo Dai 0010 — Northeastern University, College of Information Science & Engineering, Shenyang, China (and 1 more)
- Bo Dai 0011 — Beijing University of Posts and Telecommunications, School of Economics, China
- Bo Dai 0012 — China Galaxy Securities Co. Ltd., Chengdu, China
- Bo Dai 0013 — Purdue University, Department of Computer Science, West Lafayette, IN, USA
- Bo Dai 0014 — Xidian University, State Key Laboratory of Integrated Services Networks, Xi'an, China
- Bo Dai 0015 — Beihang University, School of Instrumentation Science and Opto-electronics Engineering, Beijing, China
- Bo Dai 0016 — Southeast University, School of Instrument Science and Engineering, Nanjing, China
- Bo Dai 0017
- Bo Dai 0018 — State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen, China (and 1 more)
- Bo Dai 0019 — Beijing Jiaotong University, School of Mechanical and Electronic Engineering, China
- Bo Dai 0020 — Hunan University of Technology and Business, School of Management, Changsha, China
- Bo Dai 0021
- Bo Dai 0022 — Tiangong University, School of Electrical and Electronic Engineering, Tianjin, China
- Bo Dai 0023 — Southwest University of Science and Technology, State Key Laboratory for Environmental-Friendly Energy Materials, Mianyang, China
- Bo Dai 0024 — Guilin University of Electronic Technology, School of Electronic Engineering and Automation, Guangxi, China
- Bo Dai 0025 — Peking University, School of Intelligence Science and Technology, Beijing, China (and 1 more)
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2020 – today
- 2024
- [c96]Sherry Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, Pieter Abbeel:
Probabilistic Adaptation of Black-Box Text-to-Video Models. ICLR 2024 - [c95]Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans:
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. ICML 2024 - [c94]Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai:
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models. ICML 2024 - [c93]Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai:
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. ICML 2024 - [i82]Shicong Cen, Jincheng Mei, Hanjun Dai, Dale Schuurmans, Yuejie Chi, Bo Dai:
Beyond Expectations: Learning with Stochastic Dominance Made Practical. CoRR abs/2402.02698 (2024) - [i81]Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai:
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models. CoRR abs/2402.08219 (2024) - [i80]Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans:
Stochastic Gradient Succeeds for Bandits. CoRR abs/2402.17235 (2024) - [i79]Haitong Ma, Zhaolin Ren, Bo Dai, Na Li:
Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint. CoRR abs/2404.05051 (2024) - [i78]Yang Hu, Haitong Ma, Bo Dai, Na Li:
Efficient Duple Perturbation Robustness in Low-rank MDPs. CoRR abs/2404.08089 (2024) - [i77]Shicong Cen, Jincheng Mei, Katayoon Goshvadi, Hanjun Dai, Tong Yang, Sherry Yang, Dale Schuurmans, Yuejie Chi, Bo Dai:
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF. CoRR abs/2405.19320 (2024) - [i76]Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans:
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. CoRR abs/2405.21043 (2024) - [i75]Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, Bo Dai:
HYDRA: Model Factorization Framework for Black-Box LLM Personalization. CoRR abs/2406.02888 (2024) - [i74]Dmitry Shribak, Chen-Xiao Gao, Yitong Li, Chenjun Xiao, Bo Dai:
Diffusion Spectral Representation for Reinforcement Learning. CoRR abs/2406.16121 (2024) - [i73]Hanjun Dai, Bethany Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans:
UQE: A Query Engine for Unstructured Databases. CoRR abs/2407.09522 (2024) - [i72]Tongzheng Ren, Haotian Sun, Antoine Moulin, Arthur Gretton, Bo Dai:
Spectral Representation for Causal Estimation with Hidden Confounders. CoRR abs/2407.10448 (2024) - 2023
- [c92]Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans:
Discrete Langevin Samplers via Wasserstein Gradient Flow. AISTATS 2023: 6290-6313 - [c91]Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai:
Learning to Optimize with Stochastic Dominance Constraints. AISTATS 2023: 8991-9009 - [c90]Tongzheng Ren, Zhaolin Ren, Na Li, Bo Dai:
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding. CDC 2023: 795-800 - [c89]Jiayi Chen, Hanjun Dai, Bo Dai, Aidong Zhang, Wei Wei:
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval. EMNLP (Findings) 2023: 9006-9025 - [c88]Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
Latent Variable Representation for Reinforcement Learning. ICLR 2023 - [c87]Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai:
Spectral Decomposition Representation for Reinforcement Learning. ICLR 2023 - [c86]Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai:
Any-scale Balanced Samplers for Discrete Space. ICLR 2023 - [c85]Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai:
Score-based Continuous-time Discrete Diffusion Models. ICLR 2023 - [c84]Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans:
Stochastic Gradient Succeeds for Bandits. ICML 2023: 24325-24360 - [c83]Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. NeurIPS 2023 - [c82]Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvári, Dale Schuurmans:
Ordering-based Conditions for Global Convergence of Policy Gradient Methods. NeurIPS 2023 - [c81]Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang:
AdaPlanner: Adaptive Planning from Feedback with Language Models. NeurIPS 2023 - [c80]Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai:
Energy-based Predictive Representations for Partially Observed Reinforcement Learning. UAI 2023: 2477-2487 - [i71]Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
The Role of Baselines in Policy Gradient Optimization. CoRR abs/2301.06276 (2023) - [i70]Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, Pieter Abbeel:
Learning Universal Policies via Text-Guided Video Generation. CoRR abs/2302.00111 (2023) - [i69]Tongzheng Ren, Zhaolin Ren, Na Li, Bo Dai:
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding. CoRR abs/2304.03907 (2023) - [i68]Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang:
AdaPlanner: Adaptive Planning from Feedback with Language Models. CoRR abs/2305.16653 (2023) - [i67]Mengjiao Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, Pieter Abbeel:
Probabilistic Adaptation of Text-to-Video Models. CoRR abs/2306.01872 (2023) - [i66]Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, Chao Zhang:
DF2: Distribution-Free Decision-Focused Learning. CoRR abs/2308.05889 (2023) - [i65]Jiayi Chen, Hanjun Dai, Bo Dai, Aidong Zhang, Wei Wei:
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval. CoRR abs/2311.00693 (2023) - [i64]Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai:
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. CoRR abs/2311.12244 (2023) - 2022
- [c79]Zhuangdi Zhu
Self-Adaptive Imitation Learning: Learning Tasks with Delayed Rewards from Sub-optimal Demonstrations. AAAI 2022: 9269-9277 - [c78]Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. AISTATS 2022: 4376-4396 - [c77]Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári:
The Curse of Passive Data Collection in Batch Reinforcement Learning. AISTATS 2022: 8413-8438 - [c76]Qifan Wang, Li Yang, Jingang Wang, Jitin Krishnan, Bo Dai, Sinong Wang, Zenglin Xu, Madian Khabsa, Hao Ma:
SMARTAVE: Structured Multimodal Transformer for Product Attribute Value Extraction. EMNLP (Findings) 2022: 263-276 - [c75]Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai:
Neural Stochastic Dual Dynamic Programming. ICLR 2022 - [c74]Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar A. Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans:
Understanding and Leveraging Overparameterization in Recursive Value Estimation. ICLR 2022 - [c73]Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai:
Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization. ICML 2022: 4605-4617 - [c72]Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. ICML 2022: 12542-12569 - [c71]Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, Bo Dai:
Making Linear MDPs Practical via Contrastive Representation Learning. ICML 2022: 26447-26466 - [c70]Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans:
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs. KDD 2022: 1472-1482 - [c69]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS 2022 - [c68]Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
The Role of Baselines in Policy Gradient Optimization. NeurIPS 2022 - [c67]Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li:
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games. NeurIPS 2022 - [c66]Tongzheng Ren, Tianjun Zhang, Csaba Szepesvári, Bo Dai:
A free lunch from the noise: Provable and practical exploration for representation learning. UAI 2022: 1686-1696 - [c65]Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Li Wei, Ed H. Chi:
Can Small Heads Help? Understanding and Improving Multi-Task Generalization. WWW 2022: 3009-3019 - [i63]Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li:
On the Effect of Log-Barrier Regularization in Decentralized Softmax Gradient Play in Multiagent Systems. CoRR abs/2202.00872 (2022) - [i62]Dylan Slack, Yinlam Chow, Bo Dai, Nevan Wichers:
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition. CoRR abs/2202.04849 (2022) - [i61]Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans:
Discrete Langevin Sampler via Wasserstein Gradient Flow. CoRR abs/2206.14897 (2022) - [i60]Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph E. Gonzalez
Making Linear MDPs Practical via Contrastive Representation Learning. CoRR abs/2207.07150 (2022) - [i59]Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph E. Gonzalez
Spectral Decomposition Representation for Reinforcement Learning. CoRR abs/2208.09515 (2022) - [i58]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR abs/2211.02016 (2022) - [i57]Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai:
Learning to Optimize with Stochastic Dominance Constraints. CoRR abs/2211.07767 (2022) - [i56]Haoran Sun, Lijun Yu
Score-based Continuous-time Discrete Diffusion Models. CoRR abs/2211.16750 (2022) - [i55]Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
Latent Variable Representation for Reinforcement Learning. CoRR abs/2212.08765 (2022) - 2021
- [c64]Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao:
Learning to Defend by Learning to Attack. AISTATS 2021: 577-585 - [c63]Haoming Jiang, Bo Dai, Mengjiao Yang, Tuo Zhao, Wei Wei:
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach. EMNLP (1) 2021: 7419-7451 - [c62]Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai:
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization. ICML 2021: 1760-1770 - [c61]Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
Leveraging Non-uniformity in First-order Non-convex Optimization. ICML 2021: 7555-7564 - [c60]Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou:
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs. ICML 2021: 8959-8970 - [c59]Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. ICML 2021: 11362-11371 - [c58]Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai:
Towards understanding retrosynthesis by energy-based models. NeurIPS 2021: 10186-10194 - [c57]Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi:
Nearly Horizon-Free Offline Reinforcement Learning. NeurIPS 2021: 15621-15634 - [c56]Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
Understanding the Effect of Stochasticity in Policy Optimization. NeurIPS 2021: 19339-19351 - [c55]Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai:
Combiner: Full Attention Transformer with Sparse Computation Cost. NeurIPS 2021: 22470-22482 - [i54]Haoming Jiang, Bo Dai, Mengjiao Yang, Wei Wei, Tuo Zhao:
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach. CoRR abs/2102.10242 (2021) - [i53]Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou:
Off-Policy Imitation Learning from Observations. CoRR abs/2102.13185 (2021) - [i52]Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi:
Nearly Horizon-Free Offline Reinforcement Learning. CoRR abs/2103.14077 (2021) - [i51]Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. CoRR abs/2104.02293 (2021) - [i50]Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
Leveraging Non-uniformity in First-order Non-convex Optimization. CoRR abs/2105.06072 (2021) - [i49]Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári:
On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data. CoRR abs/2106.09973 (2021) - [i48]Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai:
Combiner: Full Attention Transformer with Sparse Computation Cost. CoRR abs/2107.05768 (2021) - [i47]Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans:
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs. CoRR abs/2110.14890 (2021) - [i46]Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
Understanding the Effect of Stochasticity in Policy Optimization. CoRR abs/2110.15572 (2021) - [i45]Tongzheng Ren, Tianjun Zhang, Csaba Szepesvári, Bo Dai:
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning. CoRR abs/2111.11485 (2021) - [i44]Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai:
Neural Stochastic Dual Dynamic Programming. CoRR abs/2112.00874 (2021) - [i43]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. CoRR abs/2112.12320 (2021) - 2020
- [c54]Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song:
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees. ICLR 2020 - [c53]Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans:
GenDICE: Generalized Offline Estimation of Stationary Values. ICLR 2020 - [c52]Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans:
Scalable Deep Generative Modeling for Sparse Graphs. ICML 2020: 2302-2312 - [c51]Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans:
Batch Stationary Distribution Estimation. ICML 2020: 10203-10213 - [c50]Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans:
Energy-Based Processes for Exchangeable Data. ICML 2020: 10681-10692 - [c49]Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. NeurIPS 2020 - [c48]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. NeurIPS 2020 - [c47]Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar, Zhaoran Wang:
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach. NeurIPS 2020 - [c46]Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
Escaping the Gravitational Pull of Softmax. NeurIPS 2020 - [c45]Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister:
Differentiable Top-k with Optimal Transport. NeurIPS 2020 - [c44]Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans:
Off-Policy Evaluation via the Regularized Lagrangian. NeurIPS 2020 - [c43]Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou:
Off-Policy Imitation Learning from Observations. NeurIPS 2020 - [i42]Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song:
Retrosynthesis Prediction with Conditional Graph Logic Network. CoRR abs/2001.01408 (2020) - [i41]Ofir Nachum, Bo Dai:
Reinforcement Learning via Fenchel-Rockafellar Duality. CoRR abs/2001.01866 (2020) - [i40]Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister:
Differentiable Top-k Operator with Optimal Transport. CoRR abs/2002.06504 (2020) - [i39]Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans:
GenDICE: Generalized Offline Estimation of Stationary Values. CoRR abs/2002.09072 (2020) - [i38]Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans:
Batch Stationary Distribution Estimation. CoRR abs/2003.00722 (2020) - [i37]Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans:
Energy-Based Processes for Exchangeable Data. CoRR abs/2003.07521 (2020) - [i36]Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou:
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations. CoRR abs/2004.00530 (2020) - [i35]Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans:
Scalable Deep Generative Modeling for Sparse Graphs. CoRR abs/2006.15502 (2020) - [i34]Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, Mladen Kolar:
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach. CoRR abs/2007.01290 (2020) - [i33]Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans:
Off-Policy Evaluation via the Regularized Lagrangian. CoRR abs/2007.03438 (2020) - [i32]Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai:
Energy-based View of Retrosynthesis. CoRR abs/2007.13437 (2020) - [i31]Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Ed H. Chi:
Small Towers Make Big Differences. CoRR abs/2008.05808 (2020) - [i30]Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. CoRR abs/2010.11652 (2020) - [i29]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. CoRR abs/2011.05363 (2020) - [i28]Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans:
Offline Policy Selection under Uncertainty. CoRR abs/2012.06919 (2020)
2010 – 2019
- 2019
- [c42]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS 2019: 2321-2330 - [c41]Zhehui Chen, Haoming Jiang, Yuyang Shi, Bo Dai, Tuo Zhao:
Learning to Defense by Learning to Attack. DGS@ICLR 2019 - [c40]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR 2019 - [c39]Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li:
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. NeurIPS 2019: 2315-2325 - [c38]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS 2019: 8499-8511 - [c37]Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song:
Retrosynthesis Prediction with Conditional Graph Logic Network. NeurIPS 2019: 8870-8880 - [c36]Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. NeurIPS 2019: 10977-10988 - [c35]Albert E. Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai:
Meta Architecture Search. NeurIPS 2019: 11225-11235 - [i27]Binghong Chen, Bo Dai, Le Song:
Learning to Plan via Neural Exploration-Exploitation Trees. CoRR abs/1903.00070 (2019) - [i26]Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. CoRR abs/1904.12083 (2019) - [i25]Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru:
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification. CoRR abs/1906.00291 (2019) - [i24]Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li:
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. CoRR abs/1906.04733 (2019) - [i23]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR abs/1910.14265 (2019) - [i22]Patrick H. Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai:
Overcoming Catastrophic Forgetting by Generative Regularization. CoRR abs/1912.01238 (2019) - [i21]Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, Dale Schuurmans:
AlgaeDICE: Policy Gradient from Arbitrary Experience. CoRR abs/1912.02074 (2019) - 2018
- [b1]Bo Dai:
Learning over functions, distributions and dynamics via stochastic optimization. Georgia Institute of Technology, Atlanta, GA, USA, 2018 - [c34]Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, Haesun Park:
Multi-scale Nystrom Method. AISTATS 2018: 68-76 - [c33]Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg
Decoupled Networks. CVPR 2018: 2771-2779 - [c32]Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song:
Boosting the Actor with Dual Critic. ICLR (Poster) 2018 - [c31]Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song:
Syntax-Directed Variational Autoencoder for Structured Data. ICLR (Poster) 2018 - [c30]Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song:
Learning Steady-States of Iterative Algorithms over Graphs. ICML 2018: 1114-1122 - [c29]Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song:
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. ICML 2018: 1133-1142 - [c28]Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg
Towards Black-box Iterative Machine Teaching. ICML 2018: 3147-3155 - [c27]Hao Liu, Lirong He, Haoli Bai, Bo Dai, Kun Bai, Zenglin Xu:
Structured Inference for Recurrent Hidden Semi-markov Model. IJCAI 2018: 2447-2453 - [c26]Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru:
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification. NeurIPS 2018: 4130-4140 - [c25]Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song:
Learning towards Minimum Hyperspherical Energy. NeurIPS 2018: 6225-6236 - [c24]Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song:
Coupled Variational Bayes via Optimization Embedding. NeurIPS 2018: 9713-9723 - [c23]Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He:
Predictive Approximate Bayesian Computation via Saddle Points. NeurIPS 2018: 10282-10291 - [c22]Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha:
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. UAI 2018: 83-92 - [i20]Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song:
Syntax-Directed Variational Autoencoder for Structured Data. CoRR abs/1802.08786 (2018) - [i19]Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song:
Decoupled Networks. CoRR abs/1804.08071 (2018) - [i18]Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song:
Learning towards Minimum Hyperspherical Energy. CoRR abs/1805.09298 (2018) - [i17]Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha:
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. CoRR abs/1807.08237 (2018) - [i16]Zhehui Chen, Haoming Jiang, Bo Dai, Tuo Zhao:
Learning to Defense by Learning to Attack. CoRR abs/1811.01213 (2018) - [i15]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. CoRR abs/1811.02228 (2018) - [i14]Albert E. Shaw, Bo Dai, Weiyang Liu, Le Song:
Bayesian Meta-network Architecture Learning. CoRR abs/1812.09584 (2018) - 2017
- [c21]Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song:
Learning from Conditional Distributions via Dual Embeddings. AISTATS 2017: 1458-1467 - [c20]Hanjun Dai, Bo Dai, Yan-Ming Zhang, Shuang Li, Le Song:
Recurrent Hidden Semi-Markov Model. ICLR (Poster) 2017 - [c19]Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. ICML 2017: 913-922 - [c18]Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song:
Iterative Machine Teaching. ICML 2017: 2149-2158 - [c17]Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren
Learning from semantically dependent multi-tasks. IJCNN 2017: 3498-3505 - [c16]Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhen Liu, Bo Dai, Tuo Zhao, Le Song:
Deep Hyperspherical Learning. NIPS 2017: 3950-3960 - [i13]Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. CoRR abs/1701.02815 (2017) - [i12]Weiyang Liu, Bo Dai, James M. Rehg, Le Song:
Iterative Machine Teaching. CoRR abs/1705.10470 (2017) - [i11]Weiyang Liu, Bo Dai, Xingguo Li, James M. Rehg, Le Song:
Towards Black-box Iterative Machine Teaching. CoRR abs/1710.07742 (2017) - [i10]Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song:
Deep Hyperspherical Learning. CoRR abs/1711.03189 (2017) - [i9]Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song:
Boosting the Actor with Dual Critic. CoRR abs/1712.10282 (2017) - [i8]Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Jianshu Chen, Le Song:
Smoothed Dual Embedding Control. CoRR abs/1712.10285 (2017) - 2016
- [j3]Xin Qi, Qing Yang, David T. Nguyen, Ge Peng, Gang Zhou, Bo Dai, Daqing Zhang, Yantao Li:
A Context-Aware Framework for Reducing Bandwidth Usage of Mobile Video Chats. IEEE Trans. Multim. 18(8): 1640-1649 (2016) - [c15]Bo Dai, Niao He, Hanjun Dai, Le Song:
Provable Bayesian Inference via Particle Mirror Descent. AISTATS 2016: 985-994 - [c14]Hanjun Dai, Bo Dai, Le Song:
Discriminative Embeddings of Latent Variable Models for Structured Data. ICML 2016: 2702-2711 - [i7]Hanjun Dai, Bo Dai, Le Song:
Discriminative Embeddings of Latent Variable Models for Structured Data. CoRR abs/1603.05629 (2016) - [i6]Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song:
Learning from Conditional Distributions via Dual Kernel Embeddings. CoRR abs/1607.04579 (2016) - 2015
- [i5]Bo Dai, Niao He, Hanjun Dai, Le Song:
Scalable Bayesian Inference via Particle Mirror Descent. CoRR abs/1506.03101 (2015) - 2014
- [j2]Gang Niu, Bo Dai, Makoto Yamada
Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization. Neural Comput. 26(8): 1717-1762 (2014) - [c13]Le Song, Animashree Anandkumar, Bo Dai, Bo Xie:
Nonparametric Estimation of Multi-View Latent Variable Models. ICML 2014: 640-648 - [c12]Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Transductive Learning with Multi-class Volume Approximation. ICML 2014: 1377-1385 - [c11]Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, Le Song:
Scalable Kernel Methods via Doubly Stochastic Gradients. NIPS 2014: 3041-3049 - [i4]Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Transductive Learning with Multi-class Volume Approximation. CoRR abs/1402.0288 (2014) - [i3]Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, Le Song:
Scalable Kernel Methods via Doubly Stochastic Gradients. CoRR abs/1407.5599 (2014) - 2013
- [j1]Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama:
Maximum volume clustering: a new discriminative clustering approach. J. Mach. Learn. Res. 14(1): 2641-2687 (2013) - [c10]Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama:
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. ICML (3) 2013: 10-18 - [c9]Le Song, Bo Dai:
Robust Low Rank Kernel Embeddings of Multivariate Distributions. NIPS 2013: 3228-3236 - [i2]Le Song, Animashree Anandkumar, Bo Dai, Bo Xie:
Nonparametric Estimation of Multi-View Latent Variable Models. CoRR abs/1311.3287 (2013) - 2012
- [c8]Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama:
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. ICML 2012 - [i1]Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama:
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. CoRR abs/1206.4614 (2012) - 2011
- [c7]Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama:
Maximum Volume Clustering. AISTATS 2011: 561-569 - 2010
- [c6]Bo Dai, Bao-Gang Hu
Bayesian Maximum Margin Clustering. ICDM 2010: 108-117 - [c5]Yajun Qu, Bo Dai, Bao-Gang Hu
Neural-network based regression model with prior from ranking information. IJCNN 2010: 1-8 - [c4]Jun Jiao, Chen Shen, Bo Dai, Xuan Mo:
A Multiple Instance Approach for Keyword-Based Retrieval in Un-annotated Image Database. MMM 2010: 749-754 - [c3]Gang Niu, Bo Dai, Lin Shang, Yangsheng Ji:
Rough Margin Based Core Vector Machine. PAKDD (1) 2010: 134-141 - [c2]Bo Dai, Gang Niu:
Compact Margin Machine. PAKDD (2) 2010: 507-514 - [c1]Bo Dai, Bao-Gang Hu:
Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering. ACML 2010: 47-62
Coauthor Index
[c96] [c95] [c93] [i82] [i80] [i77] [i76] [i73] [c92] [c91] [c88] [c87] [c86] [c85] [c84] [c83] [c82] [c80] [i71] [i70] [i67] [i64] [c78] [c77] [c75] [c74] [c73] [c71] [c70] [c68] [c67] [i63] [i61] [i60] [i59] [i57] [i56] [i55] [c61] [c60] [c59] [c56] [c55] [i51] [i50] [i49] [i48] [i47] [i46] [i44] [c53] [c52] [c51] [c50] [c49] [c48] [c46] [c44] [i39] [i38] [i37] [i35] [i33] [i30] [i29] [i28] [c42] [c36] [i26] [i21] [i15]
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