Hanjun Dai

According to our database, Hanjun Dai authored at least 94 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

SDDBench: A Benchmark for Synthesizable Drug Design.

Songtao Liu, Zhengkai Tu, Hanjun Dai, Peng Liu

CoRR, 2024

Matryoshka: Learning to Drive Black-Box LLMs with LLMs.

Changhao Li, Yuchen Zhuang, Rushi Qiang, Haotian Sun, Hanjun Dai, Chao Zhang, Bo Dai

CoRR, 2024

Faster WIND: Accelerating Iterative Best-of-<i>N</i> Distillation for LLM Alignment.

Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen, Dale Schuurmans, Yuejie Chi, Bo Dai

CoRR, 2024

Autoregressive Large Language Models are Computationally Universal.

Dale Schuurmans, Hanjun Dai, Francesco Zanini

CoRR, 2024

UQE: A Query Engine for Unstructured Databases.

Hanjun Dai, Bethany Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans

CoRR, 2024

Exploring and Benchmarking the Planning Capabilities of Large Language Models.

Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi

CoRR, 2024

Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models.

Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu

CoRR, 2024

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

CoRR, 2024

Beyond Expectations: Learning with Stochastic Dominance Made Practical.

Shicong Cen, Jincheng Mei, Hanjun Dai, Dale Schuurmans, Yuejie Chi, Bo Dai

CoRR, 2024

TYGR: Type Inference on Stripped Binaries using Graph Neural Networks.

Chang Zhu, Ziyang Li, Anton Xue, Ati Priya Bajaj, Wil Gibbs, Yibo Liu, Rajeev Alur, Tiffany Bao, Hanjun Dai, Adam Doupé, Mayur Naik, Yan Shoshitaishvili, Ruoyu Wang, Aravind Machiry

Proceedings of the 33rd USENIX Security Symposium, 2024

Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models.

Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Large Language Models can Learn Rules.

Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou, Jian Tang, Dale Schuurmans, Hanjun Dai

CoRR, 2023

Document Entity Retrieval with Massive and Noisy Pre-training.

Lijun Yu, Jin Miao, Xiaoyu Sun, Jiayi Chen, Alexander G. Hauptmann, Hanjun Dai, Wei Wei

CoRR, 2023

SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL.

Ruoxi Sun, Sercan Ö. Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, Tomas Pfister

CoRR, 2023

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets.

Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan

CoRR, 2023

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets.

Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas.

Kensen Shi, Hanjun Dai, Wen-Ding Li, Kevin Ellis, Charles Sutton

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Video Timeline Modeling For News Story Understanding.

Meng Liu, Mingda Zhang, Jialu Liu, Hanjun Dai, Ming-Hsuan Yang, Shuiwang Ji, Zheyun Feng, Boqing Gong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DISCS: A Benchmark for Discrete Sampling.

Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Grathwohl, Dale Schuurmans, Hanjun Dai

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Universal Policies via Text-Guided Video Generation.

Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, Pieter Abbeel

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Revisiting Sampling for Combinatorial Optimization.

Haoran Sun, Katayoon Goshvadi, Azade Nova, Dale Schuurmans, Hanjun Dai

Proceedings of the International Conference on Machine Learning, 2023

Gradient-Free Structured Pruning with Unlabeled Data.

Azade Nova, Hanjun Dai, Dale Schuurmans

Proceedings of the International Conference on Machine Learning, 2023

Score-based Continuous-time Discrete Diffusion Models.

Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Any-scale Balanced Samplers for Discrete Space.

Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai

Proceedings of the Eleventh International Conference on Learning Representations, 2023

DocumentNet: Bridging the Data Gap in Document Pre-training.

Lijun Yu, Jin Miao, Xiaoyu Sun, Jiayi Chen, Alexander G. Hauptmann, Hanjun Dai, Wei Wei

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023, 2023

Universal Self-Adaptive Prompting.

Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Eisenschlos, Sercan Ö. Arik, Tomas Pfister

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data.

Ruoxi Sun, Sercan Ö. Arik, Rajarishi Sinha, Hootan Nakhost, Hanjun Dai, Pengcheng Yin, Tomas Pfister

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval.

Jiayi Chen, Hanjun Dai, Bo Dai, Aidong Zhang, Wei Wei

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Discrete Langevin Samplers via Wasserstein Gradient Flow.

Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Learning to Optimize with Stochastic Dominance Constraints.

Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Better Zero-Shot Reasoning with Self-Adaptive Prompting.

Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

Learning to Optimize with Stochastic Dominance Constraints.

Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai

CoRR, 2022

Annealed Training for Combinatorial Optimization on Graphs.

Haoran Sun, Etash Kumar Guha, Hanjun Dai

CoRR, 2022

Discrete Langevin Sampler via Wasserstein Gradient Flow.

Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans

CoRR, 2022

Does GNN Pretraining Help Molecular Representation?

Ruoxi Sun, Hanjun Dai, Adams Wei Yu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Optimal Scaling for Locally Balanced Proposals in Discrete Spaces.

Haoran Sun, Hanjun Dai, Dale Schuurmans

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs.

Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization.

Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai

Proceedings of the International Conference on Machine Learning, 2022

Path Auxiliary Proposal for MCMC in Discrete Space.

Haoran Sun, Hanjun Dai, Wei Xia, Arun Ramamurthy

Proceedings of the Tenth International Conference on Learning Representations, 2022

CrossBeam: Learning to Search in Bottom-Up Program Synthesis.

Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton

Proceedings of the Tenth International Conference on Learning Representations, 2022

CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation.

Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik

Proceedings of the Tenth International Conference on Learning Representations, 2022

Neural Stochastic Dual Dynamic Programming.

Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Towards understanding retrosynthesis by energy-based models.

Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Combiner: Full Attention Transformer with Sparse Computation Cost.

Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs.

Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou

Proceedings of the 38th International Conference on Machine Learning, 2021

SpreadsheetCoder: Formula Prediction from Semi-structured Context.

Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou

Proceedings of the 38th International Conference on Machine Learning, 2021

BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration.

Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai

Proceedings of the 9th International Conference on Learning Representations, 2021

Molecule Optimization by Explainable Evolution.

Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Learning Neural Algorithms with Graph Structures.

Hanjun Dai

PhD thesis, 2020

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders.

Rohit Batra, Hanjun Dai, Tran Doan Huan, Lihua Chen, Chiho Kim, Will R. Gutekunst, Le Song, Rampi Ramprasad

CoRR, 2020

Energy-based View of Retrosynthesis.

Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai

CoRR, 2020

Differentiable Top-k Operator with Optimal Transport.

Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

CoRR, 2020

Differentiable Top-k with Optimal Transport.

Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration.

Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Energy-Based Processes for Exchangeable Data.

Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans

Proceedings of the 37th International Conference on Machine Learning, 2020

Scalable Deep Generative Modeling for Sparse Graphs.

Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans

Proceedings of the 37th International Conference on Machine Learning, 2020

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search.

Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning To Stop While Learning To Predict.

Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song

Proceedings of the 37th International Conference on Machine Learning, 2020

Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs.

Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang

Proceedings of the 8th International Conference on Learning Representations, 2020

Code2Inv: A Deep Learning Framework for Program Verification.

Xujie Si, Aaditya Naik, Hanjun Dai, Mayur Naik, Le Song

Proceedings of the Computer Aided Verification - 32nd International Conference, 2020

2019

Meta Particle Flow for Sequential Bayesian Inference.

Xinshi Chen, Hanjun Dai, Le Song

CoRR, 2019

Learning Transferable Graph Exploration.

Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exponential Family Estimation via Adversarial Dynamics Embedding.

Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Retrosynthesis Prediction with Conditional Graph Logic Network.

Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

CompILE: Compositional Imitation Learning and Execution.

Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia

Proceedings of the 36th International Conference on Machine Learning, 2019

Particle Flow Bayes' Rule.

Xinshi Chen, Hanjun Dai, Le Song

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning a Meta-Solver for Syntax-Guided Program Synthesis.

Xujie Si, Yuan Yang, Hanjun Dai, Mayur Naik, Le Song

Proceedings of the 7th International Conference on Learning Representations, 2019

Kernel Exponential Family Estimation via Doubly Dual Embedding.

Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Compositional Imitation Learning: Explaining and executing one task at a time.

Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia

CoRR, 2018

KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings.

Yuyu Zhang, Hanjun Dai, Kamil Toraman, Le Song

CoRR, 2018

Learning Loop Invariants for Program Verification.

Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification.

Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Coupled Variational Bayes via Optimization Embedding.

Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Adversarial Attack on Graph Structured Data.

Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song

Proceedings of the 35th International Conference on Machine Learning, 2018

Learning Steady-States of Iterative Algorithms over Graphs.

Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song

Proceedings of the 35th International Conference on Machine Learning, 2018

Syntax-Directed Variational Autoencoder for Structured Data.

Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song

Proceedings of the 6th International Conference on Learning Representations, 2018

Additive Margin Softmax for Face Verification.

Feng Wang, Weiyang Liu, Hanjun Dai, Haijun Liu, Jian Cheng

Proceedings of the 6th International Conference on Learning Representations, 2018

Variational Reasoning for Question Answering With Knowledge Graph.

Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Know-Evolve: Deep Reasoning in Temporal Knowledge Graphs.

Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song

CoRR, 2017

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.

Hanjun Dai, Ramzan Umarov, Hiroyuki Kuwahara, Yu Li, Le Song, Xin Gao

Bioinform., 2017

Learning Combinatorial Optimization Algorithms over Graphs.

Elias B. Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, Le Song

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs.

Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song

Proceedings of the 34th International Conference on Machine Learning, 2017

Recurrent Hidden Semi-Markov Model.

Hanjun Dai, Bo Dai, Yan-Ming Zhang, Shuang Li, Le Song

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Recurrent Coevolutionary Feature Embedding Processes for Recommendation.

Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song

CoRR, 2016

Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation.

Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song

Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016

Recurrent Marked Temporal Point Processes: Embedding Event History to Vector.

Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, Le Song

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Discriminative Embeddings of Latent Variable Models for Structured Data.

Hanjun Dai, Bo Dai, Le Song

Proceedings of the 33nd International Conference on Machine Learning, 2016

Provable Bayesian Inference via Particle Mirror Descent.

Bo Dai, Niao He, Hanjun Dai, Le Song

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge.

Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Hanjun Dai, Tie-Yan Liu

ACM Trans. Inf. Syst., 2015

Online Supervised Subspace Tracking.

Yao Xie, Ruiyang Song, Hanjun Dai, Qingbin Li, Le Song

CoRR, 2015

Scalable Bayesian Inference via Particle Mirror Descent.

Bo Dai, Niao He, Hanjun Dai, Le Song

CoRR, 2015

M-Statistic for Kernel Change-Point Detection.

Shuang Li, Yao Xie, Hanjun Dai, Le Song

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

A Probabilistic Model for Learning Multi-Prototype Word Embeddings.

Fei Tian, Hanjun Dai, Jiang Bian, Bin Gao, Rui Zhang, Enhong Chen, Tie-Yan Liu

Proceedings of the COLING 2014, 2014

Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks.

Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014