Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. - Yuyu Zhang; Hanjun Dai; Chang Xu 008; Jun Feng; Taifeng Wang; Jiang Bian 002; Bin Wang 004; Tie-Yan Liu (AAAI, 2014)

A Probabilistic Model for Learning Multi-Prototype Word Embeddings. - Fei Tian; Hanjun Dai; Jiang Bian 002; Bin Gao 001; Rui Zhang 052; Enhong Chen; Tie-Yan Liu (COLING, 2014)

M-Statistic for Kernel Change-Point Detection. - Shuang Li 002; Yao Xie 002; Hanjun Dai; Le Song (NIPS, 2015)

Scalable Bayesian Inference via Particle Mirror Descent. - Bo Dai 001; Niao He; Hanjun Dai; Le Song (CoRR, 2015)

Online Supervised Subspace Tracking. - Yao Xie 002; Ruiyang Song; Hanjun Dai; Qingbin Li; Le Song (CoRR, 2015)

KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge. - Qing Cui; Bin Gao 001; Jiang Bian 002; Siyu Qiu; Hanjun Dai; Tie-Yan Liu (ACM Trans. Inf. Syst., 2015)

Provable Bayesian Inference via Particle Mirror Descent. - Bo Dai 001; Niao He; Hanjun Dai; Le Song (AISTATS, 2016)

Discriminative Embeddings of Latent Variable Models for Structured Data. - Hanjun Dai; Bo Dai 001; Le Song (ICML, 2016)

Recurrent Marked Temporal Point Processes: Embedding Event History to Vector. - Nan Du 002; Hanjun Dai; Rakshit Trivedi; Utkarsh Upadhyay; Manuel Gomez-Rodriguez; Le Song (KDD, 2016)

Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation. - Hanjun Dai; Yichen Wang 001; Rakshit Trivedi; Le Song (DLRS@RecSys, 2016)

Recurrent Coevolutionary Feature Embedding Processes for Recommendation. - Hanjun Dai; Yichen Wang 001; Rakshit Trivedi; Le Song (CoRR, 2016)

Recurrent Hidden Semi-Markov Model. - Hanjun Dai; Bo Dai 001; Yan-Ming Zhang; Shuang Li 002; Le Song (ICLR (Poster), 2017)

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. - Rakshit Trivedi; Hanjun Dai; Yichen Wang 001; Le Song (ICML, 2017)

Learning Combinatorial Optimization Algorithms over Graphs. - Elias B. Khalil; Hanjun Dai; Yuyu Zhang; Bistra Dilkina; Le Song (NIPS, 2017)

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape. - Hanjun Dai; Ramzan Umarov; Hiroyuki Kuwahara; Yu Li 006; Le Song; Xin Gao 001 (Bioinform., 2017)

Know-Evolve: Deep Reasoning in Temporal Knowledge Graphs. - Rakshit Trivedi; Hanjun Dai; Yichen Wang 001; Le Song (CoRR, 2017)

Variational Reasoning for Question Answering With Knowledge Graph. - Yuyu Zhang; Hanjun Dai; Zornitsa Kozareva; Alexander J. Smola; Le Song (AAAI, 2018)

Additive Margin Softmax for Face Verification. - Feng Wang 015; Weiyang Liu; Hanjun Dai; Haijun Liu 001; Jian Cheng 003 (ICLR (Workshop), 2018)

Syntax-Directed Variational Autoencoder for Structured Data. - Hanjun Dai; Yingtao Tian; Bo Dai 001; Steven Skiena; Le Song (ICLR (Poster), 2018)

Learning Steady-States of Iterative Algorithms over Graphs. - Hanjun Dai; Zornitsa Kozareva; Bo Dai 001; Alexander J. Smola; Le Song (ICML, 2018)

Adversarial Attack on Graph Structured Data. - Hanjun Dai; Hui Li; Tian Tian 001; Xin Huang; Lin Wang; Jun Zhu 001; Le Song (ICML, 2018)

Coupled Variational Bayes via Optimization Embedding. - Bo Dai 001; Hanjun Dai; Niao He; Weiyang Liu; Zhen Liu 019; Jianshu Chen; Lin Xiao; Le Song (NeurIPS, 2018)

Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification. - Harsh Shrivastava 001; Eugene Bart; Bob Price; Hanjun Dai; Bo Dai 001; Srinivas Aluru (NeurIPS, 2018)

Learning Loop Invariants for Program Verification. - Xujie Si; Hanjun Dai; Mukund Raghothaman; Mayur Naik; Le Song (NeurIPS, 2018)

KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings. - Yuyu Zhang; Hanjun Dai; Kamil Toraman; Le Song (CoRR, 2018)

Compositional Imitation Learning: Explaining and executing one task at a time. - Thomas Kipf; Yujia Li 001; Hanjun Dai; Vinícius Flores Zambaldi; Edward Grefenstette; Pushmeet Kohli; Peter W. Battaglia (CoRR, 2018)

Kernel Exponential Family Estimation via Doubly Dual Embedding. - Bo Dai 001; Hanjun Dai; Arthur Gretton; Le Song; Dale Schuurmans; Niao He (AISTATS, 2019)

Learning a Meta-Solver for Syntax-Guided Program Synthesis. - Xujie Si; Yuan Yang; Hanjun Dai; Mayur Naik; Le Song (ICLR (Poster), 2019)

Particle Flow Bayes' Rule. - Xinshi Chen; Hanjun Dai; Le Song (ICML, 2019)

CompILE: Compositional Imitation Learning and Execution. - Thomas Kipf; Yujia Li 001; Hanjun Dai; Vinícius Flores Zambaldi; Alvaro Sanchez-Gonzalez; Edward Grefenstette; Pushmeet Kohli; Peter W. Battaglia (ICML, 2019)

Retrosynthesis Prediction with Conditional Graph Logic Network. - Hanjun Dai; Chengtao Li; Connor W. Coley; Bo Dai 001; Le Song (NeurIPS, 2019)

Exponential Family Estimation via Adversarial Dynamics Embedding. - Bo Dai 001; Zhen Liu 019; Hanjun Dai; Niao He; Arthur Gretton; Le Song; Dale Schuurmans (NeurIPS, 2019)

Learning Transferable Graph Exploration. - Hanjun Dai; Yujia Li 001; Chenglong Wang; Rishabh Singh; Po-Sen Huang; Pushmeet Kohli (NeurIPS, 2019)

Meta Particle Flow for Sequential Bayesian Inference. - Xinshi Chen; Hanjun Dai; Le Song (CoRR, 2019)

Code2Inv: A Deep Learning Framework for Program Verification. - Xujie Si; Aaditya Naik; Hanjun Dai; Mayur Naik; Le Song (CAV (2), 2020)

Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs. - Elizabeth Dinella; Hanjun Dai; Ziyang Li; Mayur Naik; Le Song; Ke Wang (ICLR, 2020)

Learning To Stop While Learning To Predict. - Xinshi Chen; Hanjun Dai; Yu Li 006; Xin Gao 001; Le Song (ICML, 2020)

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search. - Binghong Chen; Chengtao Li; Hanjun Dai; Le Song (ICML, 2020)

Scalable Deep Generative Modeling for Sparse Graphs. - Hanjun Dai; Azade Nazi; Yujia Li; Bo Dai 001; Dale Schuurmans (ICML, 2020)

Energy-Based Processes for Exchangeable Data. - Mengjiao Yang 001; Bo Dai 001; Hanjun Dai; Dale Schuurmans (ICML, 2020)

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. - Hanjun Dai; Rishabh Singh; Bo Dai 001; Charles Sutton; Dale Schuurmans (NeurIPS, 2020)

Differentiable Top-k with Optimal Transport. - Yujia Xie; Hanjun Dai; Minshuo Chen; Bo Dai 001; Tuo Zhao; Hongyuan Zha; Wei Wei 019; Tomas Pfister (NeurIPS, 2020)

Differentiable Top-k Operator with Optimal Transport. - Yujia Xie; Hanjun Dai; Minshuo Chen; Bo Dai 001; Tuo Zhao; Hongyuan Zha; Wei Wei 019; Tomas Pfister (CoRR, 2020)

Energy-based View of Retrosynthesis. - Ruoxi Sun 002; Hanjun Dai; Li Li 060; Steven Kearnes; Bo Dai 001 (CoRR, 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)

Learning Neural Algorithms with Graph Structures. - Hanjun Dai (2020)

Molecule Optimization by Explainable Evolution. - Binghong Chen; Tianzhe Wang; Chengtao Li; Hanjun Dai; Le Song (ICLR, 2021)

BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. - Augustus Odena; Kensen Shi; David Bieber; Rishabh Singh; Charles Sutton; Hanjun Dai (ICLR, 2021)

SpreadsheetCoder: Formula Prediction from Semi-structured Context. - Xinyun Chen; Petros Maniatis; Rishabh Singh; Charles Sutton; Hanjun Dai; Max Lin; Denny Zhou (ICML, 2021)

LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs. - Hongyu Ren; Hanjun Dai; Bo Dai 001; Xinyun Chen; Michihiro Yasunaga; Haitian Sun; Dale Schuurmans; Jure Leskovec; Denny Zhou (ICML, 2021)

Combiner: Full Attention Transformer with Sparse Computation Cost. - Hongyu Ren; Hanjun Dai; Zihang Dai; Mengjiao Yang 001; Jure Leskovec; Dale Schuurmans; Bo Dai 001 (NeurIPS, 2021)

Towards understanding retrosynthesis by energy-based models. - Ruoxi Sun 002; Hanjun Dai; Li Li 060; Steven Kearnes; Bo Dai 001 (NeurIPS, 2021)

Neural Stochastic Dual Dynamic Programming. - Hanjun Dai; Yuan Xue; Zia Syed; Dale Schuurmans; Bo Dai 001 (ICLR, 2022)

CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation. - Pardis Pashakhanloo; Aaditya Naik; Yuepeng Wang 001; Hanjun Dai; Petros Maniatis; Mayur Naik (ICLR, 2022)

CrossBeam: Learning to Search in Bottom-Up Program Synthesis. - Kensen Shi; Hanjun Dai; Kevin Ellis; Charles Sutton (ICLR, 2022)

Path Auxiliary Proposal for MCMC in Discrete Space. - Haoran Sun; Hanjun Dai; Wei Xia; Arun Ramamurthy (ICLR, 2022)

Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization. - Hanjun Dai; Mengjiao Yang 001; Yuan Xue; Dale Schuurmans; Bo Dai 001 (ICML, 2022)

SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs. - Hongyu Ren; Hanjun Dai; Bo Dai 001; Xinyun Chen; Denny Zhou; Jure Leskovec; Dale Schuurmans (KDD, 2022)

Optimal Scaling for Locally Balanced Proposals in Discrete Spaces. - Haoran Sun; Hanjun Dai; Dale Schuurmans (NeurIPS, 2022)

Does GNN Pretraining Help Molecular Representation? - Ruoxi Sun 002; Hanjun Dai; Adams Wei Yu (NeurIPS, 2022)

Discrete Langevin Sampler via Wasserstein Gradient Flow. - Haoran Sun; Hanjun Dai; Bo Dai 001; Haomin Zhou 001; Dale Schuurmans (CoRR, 2022)

Annealed Training for Combinatorial Optimization on Graphs. - Haoran Sun; Etash Kumar Guha; Hanjun Dai (CoRR, 2022)

Learning to Optimize with Stochastic Dominance Constraints. - Hanjun Dai; Yuan Xue; Niao He; Bethany Wang; Na Li 002; Dale Schuurmans; Bo Dai 001 (CoRR, 2022)

Better Zero-Shot Reasoning with Self-Adaptive Prompting. - Xingchen Wan; Ruoxi Sun 002; Hanjun Dai; Sercan Ö. Arik; Tomas Pfister (ACL (Findings), 2023)

Learning to Optimize with Stochastic Dominance Constraints. - Hanjun Dai; Yuan Xue; Niao He; Yixin Wang; Na Li 002; Dale Schuurmans; Bo Dai 001 (AISTATS, 2023)

Discrete Langevin Samplers via Wasserstein Gradient Flow. - Haoran Sun; Hanjun Dai; Bo Dai 001; Haomin Zhou 001; Dale Schuurmans (AISTATS, 2023)

On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval. - Jiayi Chen; Hanjun Dai; Bo Dai 001; Aidong Zhang; Wei Wei 019 (EMNLP (Findings), 2023)

SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data. - Ruoxi Sun 002; Sercan Ö. Arik; Rajarishi Sinha; Hootan Nakhost; Hanjun Dai; Pengcheng Yin; Tomas Pfister (EMNLP (Findings), 2023)

Universal Self-Adaptive Prompting. - Xingchen Wan; Ruoxi Sun 002; Hootan Nakhost; Hanjun Dai; Julian Eisenschlos; Sercan Ö. Arik; Tomas Pfister (EMNLP, 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 019 (EMNLP (Industry Track), 2023)

Any-scale Balanced Samplers for Discrete Space. - Haoran Sun; Bo Dai 001; Charles Sutton; Dale Schuurmans; Hanjun Dai (ICLR, 2023)

Score-based Continuous-time Discrete Diffusion Models. - Haoran Sun; Lijun Yu; Bo Dai 001; Dale Schuurmans; Hanjun Dai (ICLR, 2023)

Gradient-Free Structured Pruning with Unlabeled Data. - Azade Nova; Hanjun Dai; Dale Schuurmans (ICML, 2023)

Revisiting Sampling for Combinatorial Optimization. - Haoran Sun; Katayoon Goshvadi; Azade Nova; Dale Schuurmans; Hanjun Dai (ICML, 2023)

Learning Universal Policies via Text-Guided Video Generation. - Yilun Du; Sherry Yang 001; Bo Dai 001; Hanjun Dai; Ofir Nachum; Josh Tenenbaum 001; Dale Schuurmans; Pieter Abbeel (NeurIPS, 2023)

DISCS: A Benchmark for Discrete Sampling. - Katayoon Goshvadi; Haoran Sun; Xingchao Liu; Azade Nova; Ruqi Zhang; Will Grathwohl; Dale Schuurmans; Hanjun Dai (NeurIPS, 2023)

Video Timeline Modeling For News Story Understanding. - Meng Liu 015; Mingda Zhang; Jialu Liu; Hanjun Dai; Ming-Hsuan Yang 001; Shuiwang Ji; Zheyun Feng; Boqing Gong (NeurIPS, 2023)

LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas. - Kensen Shi; Hanjun Dai; Wen-Ding Li; Kevin Ellis; Charles Sutton (NeurIPS, 2023)

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. - Dinghuai Zhang; Hanjun Dai; Nikolay Malkin; Aaron C. Courville; Yoshua Bengio; Ling Pan (NeurIPS, 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)

SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL. - Ruoxi Sun 002; Sercan Ö. Arik; Hootan Nakhost; Hanjun Dai; Rajarishi Sinha; Pengcheng Yin; Tomas Pfister (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 019 (CoRR, 2023)

Large Language Models can Learn Rules. - Zhaocheng Zhu; Yuan Xue; Xinyun Chen; Denny Zhou; Jian Tang 005; Dale Schuurmans; Hanjun Dai (CoRR, 2023)

Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models. - Songtao Liu; Hanjun Dai; Yue Zhao 016; Peng Liu (ICML, 2024)

UQE: A Query Engine for Unstructured Databases. - Hanjun Dai; Bethany Wang; Xingchen Wan; Bo Dai 001; Sherry Yang 001; Azade Nova; Pengcheng Yin; Phitchaya Mangpo Phothilimthana; Charles Sutton; Dale Schuurmans (NeurIPS, 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; et al. (USENIX Security Symposium, 2024)

Beyond Expectations: Learning with Stochastic Dominance Made Practical. - Shicong Cen; Jincheng Mei; Hanjun Dai; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (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)

Autoregressive Large Language Models are Computationally Universal. - Dale Schuurmans; Hanjun Dai; Francesco Zanini (CoRR, 2024)

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. - Tong Yang 007; Jincheng Mei; Hanjun Dai; Zixin Wen; Shicong Cen; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (CoRR, 2024)

Matryoshka: Learning to Drive Black-Box LLMs with LLMs. - Changhao Li; Yuchen Zhuang; Rushi Qiang; Haotian Sun; Hanjun Dai; Chao Zhang 014; Bo Dai 001 (CoRR, 2024)

SDDBench: A Benchmark for Synthesizable Drug Design. - Songtao Liu; Zhengkai Tu; Hanjun Dai; Peng Liu (CoRR, 2024)

SQL-PaLM: Improved large language model adaptation for Text-to-SQL. - Ruoxi Sun 002; Sercan Ö. Arik; Alexandre Muzio; Lesly Miculicich; Satya Kesav Gundabathula; Pengcheng Yin; Hanjun Dai; Hootan Nakhost; Rajarishi Sinha; Zifeng Wang 002; Tomas Pfister (Trans. Mach. Learn. Res., 2024)

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. - Tong Yang 007; Jincheng Mei; Hanjun Dai; Zixin Wen; Shicong Cen; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (AISTATS, 2025)

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF. - Shicong Cen; Jincheng Mei; Katayoon Goshvadi; Hanjun Dai; Tong Yang 007; Sherry Yang 001; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (ICLR, 2025)

AmorLIP: Efficient Language-Image Pretraining via Amortization. - Haotian Sun; Yitong Li; Yuchen Zhuang; Niao He; Hanjun Dai; Bo Dai 001 (CoRR, 2025)

Towards Better Instruction Following Retrieval Models. - Yuchen Zhuang; Aaron Trinh; Rushi Qiang; Haotian Sun; Chao Zhang 014; Hanjun Dai; Bo Dai 001 (CoRR, 2025)