default search action
Hanjun Dai
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [c66]Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu:
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models. ICML 2024 - [c65]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. NeurIPS 2024 - [c64]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:
TYGR: Type Inference on Stripped Binaries using Graph Neural Networks. USENIX Security Symposium 2024 - [i59]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) - [i58]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) - [i57]Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu:
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models. CoRR abs/2406.02066 (2024) - [i56]Bernd Bohnet, Azade Nova, Aaron T. Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi:
Exploring and Benchmarking the Planning Capabilities of Large Language Models. CoRR abs/2406.13094 (2024) - [i55]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) - [i54]Dale Schuurmans, Hanjun Dai, Francesco Zanini:
Autoregressive Large Language Models are Computationally Universal. CoRR abs/2410.03170 (2024) - [i53]Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen, Dale Schuurmans, Yuejie Chi, Bo Dai:
Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. CoRR abs/2410.20727 (2024) - [i52]Changhao Li, Yuchen Zhuang, Rushi Qiang, Haotian Sun, Hanjun Dai, Chao Zhang, Bo Dai:
Matryoshka: Learning to Drive Black-Box LLMs with LLMs. CoRR abs/2410.20749 (2024) - [i51]Songtao Liu, Zhengkai Tu, Hanjun Dai, Peng Liu:
SDDBench: A Benchmark for Synthesizable Drug Design. CoRR abs/2411.08306 (2024) - 2023
- [c63]Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister:
Better Zero-Shot Reasoning with Self-Adaptive Prompting. ACL (Findings) 2023: 3493-3514 - [c62]Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans:
Discrete Langevin Samplers via Wasserstein Gradient Flow. AISTATS 2023: 6290-6313 - [c61]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 - [c60]Ruoxi Sun, Sercan Ö. Arik, Rajarishi Sinha, Hootan Nakhost, Hanjun Dai, Pengcheng Yin, Tomas Pfister:
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data. EMNLP (Findings) 2023: 542-550 - [c59]Lijun Yu
, Jin Miao, Xiaoyu Sun, Jiayi Chen, Alexander G. Hauptmann, Hanjun Dai, Wei Wei:
DocumentNet: Bridging the Data Gap in Document Pre-training. EMNLP (Industry Track) 2023: 707-722 - [c58]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-Adaptive Prompting. EMNLP 2023: 7437-7462 - [c57]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 - [c56]Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai:
Any-scale Balanced Samplers for Discrete Space. ICLR 2023 - [c55]Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai:
Score-based Continuous-time Discrete Diffusion Models. ICLR 2023 - [c54]Azade Nova, Hanjun Dai, Dale Schuurmans:
Gradient-Free Structured Pruning with Unlabeled Data. ICML 2023: 26326-26341 - [c53]Haoran Sun, Katayoon Goshvadi, Azade Nova, Dale Schuurmans, Hanjun Dai:
Revisiting Sampling for Combinatorial Optimization. ICML 2023: 32859-32874 - [c52]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 - [c51]Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Grathwohl, Dale Schuurmans, Hanjun Dai:
DISCS: A Benchmark for Discrete Sampling. NeurIPS 2023 - [c50]Meng Liu, Mingda Zhang, Jialu Liu, Hanjun Dai, Ming-Hsuan Yang, Shuiwang Ji, Zheyun Feng, Boqing Gong:
Video Timeline Modeling For News Story Understanding. NeurIPS 2023 - [c49]Kensen Shi, Hanjun Dai, Wen-Ding Li, Kevin Ellis, Charles Sutton:
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas. NeurIPS 2023 - [c48]Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan:
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. NeurIPS 2023 - [i50]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) - [i49]Azade Nova, Hanjun Dai, Dale Schuurmans:
Gradient-Free Structured Pruning with Unlabeled Data. CoRR abs/2303.04185 (2023) - [i48]Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister:
Better Zero-Shot Reasoning with Self-Adaptive Prompting. CoRR abs/2305.14106 (2023) - [i47]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-adaptive Prompting. CoRR abs/2305.14926 (2023) - [i46]Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan:
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets. CoRR abs/2305.17010 (2023) - [i45]Ruoxi Sun, Sercan Ö. Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, Tomas Pfister:
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL. CoRR abs/2306.00739 (2023) - [i44]Kensen Shi, Hanjun Dai, Wen-Ding Li, Kevin Ellis, Charles Sutton:
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas. CoRR abs/2306.02049 (2023) - [i43]Lijun Yu
Document Entity Retrieval with Massive and Noisy Pre-training. CoRR abs/2306.08937 (2023) - [i42]Meng Liu, Mingda Zhang, Jialu Liu, Hanjun Dai, Ming-Hsuan Yang, Shuiwang Ji, Zheyun Feng, Boqing Gong:
Video Timeline Modeling For News Story Understanding. CoRR abs/2309.13446 (2023) - [i41]Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou, Jian Tang, Dale Schuurmans, Hanjun Dai:
Large Language Models can Learn Rules. CoRR abs/2310.07064 (2023) - [i40]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) - [i39]Ruoxi Sun, Sercan Ö. Arik, Rajarishi Sinha, Hootan Nakhost, Hanjun Dai, Pengcheng Yin, Tomas Pfister:
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data. CoRR abs/2311.02883 (2023) - 2022
- [c47]Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai:
Neural Stochastic Dual Dynamic Programming. ICLR 2022 - [c46]Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik:
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation. ICLR 2022 - [c45]Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton:
CrossBeam: Learning to Search in Bottom-Up Program Synthesis. ICLR 2022 - [c44]Haoran Sun, Hanjun Dai, Wei Xia, Arun Ramamurthy:
Path Auxiliary Proposal for MCMC in Discrete Space. ICLR 2022 - [c43]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 - [c42]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 - [c41]Haoran Sun, Hanjun Dai, Dale Schuurmans:
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces. NeurIPS 2022 - [c40]Ruoxi Sun, Hanjun Dai, Adams Wei Yu:
Does GNN Pretraining Help Molecular Representation? NeurIPS 2022 - [i38]Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton:
CrossBeam: Learning to Search in Bottom-Up Program Synthesis. CoRR abs/2203.10452 (2022) - [i37]Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans:
Discrete Langevin Sampler via Wasserstein Gradient Flow. CoRR abs/2206.14897 (2022) - [i36]Ruoxi Sun, Hanjun Dai, Adams Wei Yu:
Does GNN Pretraining Help Molecular Representation? CoRR abs/2207.06010 (2022) - [i35]Haoran Sun, Etash Kumar Guha, Hanjun Dai:
Annealed Training for Combinatorial Optimization on Graphs. CoRR abs/2207.11542 (2022) - [i34]Haoran Sun, Hanjun Dai, Dale Schuurmans:
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces. CoRR abs/2209.08183 (2022) - [i33]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) - [i32]Haoran Sun, Lijun Yu
Score-based Continuous-time Discrete Diffusion Models. CoRR abs/2211.16750 (2022) - 2021
- [c39]Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song:
Molecule Optimization by Explainable Evolution. ICLR 2021 - [c38]Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai:
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. ICLR 2021 - [c37]Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. ICML 2021: 1661-1672 - [c36]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 - [c35]Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai:
Towards understanding retrosynthesis by energy-based models. NeurIPS 2021: 10186-10194 - [c34]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 - [i31]Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. CoRR abs/2106.15339 (2021) - [i30]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) - [i29]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) - [i28]Hanjun Dai, Yuan Xue, Zia Syed, Dale Schuurmans, Bo Dai:
Neural Stochastic Dual Dynamic Programming. CoRR abs/2112.00874 (2021) - 2020
- [b1]Hanjun Dai:
Learning Neural Algorithms with Graph Structures. Georgia Institute of Technology, Atlanta, GA, USA, 2020 - [c33]Xujie Si, Aaditya Naik, Hanjun Dai, Mayur Naik, Le Song:
Code2Inv: A Deep Learning Framework for Program Verification. CAV (2) 2020: 151-164 - [c32]Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang:
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs. ICLR 2020 - [c31]Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song:
Learning To Stop While Learning To Predict. ICML 2020: 1520-1530 - [c30]Binghong Chen, Chengtao Li, Hanjun Dai, Le Song:
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search. ICML 2020: 1608-1616 - [c29]Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans:
Scalable Deep Generative Modeling for Sparse Graphs. ICML 2020: 2302-2312 - [c28]Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans:
Energy-Based Processes for Exchangeable Data. ICML 2020: 10681-10692 - [c27]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. NeurIPS 2020 - [c26]Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister:
Differentiable Top-k with Optimal Transport. NeurIPS 2020 - [i27]Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song:
Retrosynthesis Prediction with Conditional Graph Logic Network. CoRR abs/2001.01408 (2020) - [i26]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) - [i25]Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans:
Energy-Based Processes for Exchangeable Data. CoRR abs/2003.07521 (2020) - [i24]Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song:
Learning to Stop While Learning to Predict. CoRR abs/2006.05082 (2020) - [i23]Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans:
Scalable Deep Generative Modeling for Sparse Graphs. CoRR abs/2006.15502 (2020) - [i22]Binghong Chen, Chengtao Li, Hanjun Dai, Le Song:
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search. CoRR abs/2006.15820 (2020) - [i21]Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai:
Energy-based View of Retrosynthesis. CoRR abs/2007.13437 (2020) - [i20]Rohit Batra, Hanjun Dai, Tran Doan Huan, Lihua Chen, Chiho Kim, Will R. Gutekunst, Le Song, Rampi Ramprasad:
Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders. CoRR abs/2011.02551 (2020) - [i19]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)
2010 – 2019
- 2019
- [c25]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS 2019: 2321-2330 - [c24]Xujie Si, Yuan Yang, Hanjun Dai, Mayur Naik, Le Song:
Learning a Meta-Solver for Syntax-Guided Program Synthesis. ICLR (Poster) 2019 - [c23]Xinshi Chen, Hanjun Dai, Le Song:
Particle Flow Bayes' Rule. ICML 2019: 1022-1031 - [c22]Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia:
CompILE: Compositional Imitation Learning and Execution. ICML 2019: 3418-3428 - [c21]Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. NeurIPS 2019: 2514-2525 - [c20]Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song:
Retrosynthesis Prediction with Conditional Graph Logic Network. NeurIPS 2019: 8870-8880 - [c19]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 - [i18]Xinshi Chen, Hanjun Dai, Le Song:
Meta Particle Flow for Sequential Bayesian Inference. CoRR abs/1902.00640 (2019) - [i17]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) - [i16]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) - [i15]Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. CoRR abs/1910.12980 (2019) - 2018
- [c18]Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song:
Variational Reasoning for Question Answering With Knowledge Graph. AAAI 2018: 6069-6076 - [c17]Feng Wang, Weiyang Liu, Hanjun Dai, Haijun Liu, Jian Cheng:
Additive Margin Softmax for Face Verification. ICLR (Workshop) 2018 - [c16]Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song:
Syntax-Directed Variational Autoencoder for Structured Data. ICLR (Poster) 2018 - [c15]Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song:
Learning Steady-States of Iterative Algorithms over Graphs. ICML 2018: 1114-1122 - [c14]Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song:
Adversarial Attack on Graph Structured Data. ICML 2018: 1123-1132 - [c13]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 - [c12]Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song:
Learning Loop Invariants for Program Verification. NeurIPS 2018: 7762-7773 - [c11]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 - [i14]Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song:
Syntax-Directed Variational Autoencoder for Structured Data. CoRR abs/1802.08786 (2018) - [i13]Yuyu Zhang, Hanjun Dai, Kamil Toraman, Le Song:
KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings. CoRR abs/1805.12393 (2018) - [i12]Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song:
Adversarial Attack on Graph Structured Data. CoRR abs/1806.02371 (2018) - [i11]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) - [i10]Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia:
Compositional Imitation Learning: Explaining and executing one task at a time. CoRR abs/1812.01483 (2018) - 2017
- [j2]Hanjun Dai, Ramzan Umarov, Hiroyuki Kuwahara, Yu Li
Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape. Bioinform. 33(22): 3575-3583 (2017) - [c10]Hanjun Dai, Bo Dai, Yan-Ming Zhang, Shuang Li, Le Song:
Recurrent Hidden Semi-Markov Model. ICLR (Poster) 2017 - [c9]Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song:
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. ICML 2017: 3462-3471 - [c8]Elias B. Khalil
Learning Combinatorial Optimization Algorithms over Graphs. NIPS 2017: 6348-6358 - [i9]Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song:
Learning Combinatorial Optimization Algorithms over Graphs. CoRR abs/1704.01665 (2017) - [i8]Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song:
Know-Evolve: Deep Reasoning in Temporal Knowledge Graphs. CoRR abs/1705.05742 (2017) - [i7]Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song:
Variational Reasoning for Question Answering with Knowledge Graph. CoRR abs/1709.04071 (2017) - 2016
- [c7]Bo Dai, Niao He, Hanjun Dai, Le Song:
Provable Bayesian Inference via Particle Mirror Descent. AISTATS 2016: 985-994 - [c6]Hanjun Dai, Bo Dai, Le Song:
Discriminative Embeddings of Latent Variable Models for Structured Data. ICML 2016: 2702-2711 - [c5]Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector. KDD 2016: 1555-1564 - [c4]Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song:
Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation. DLRS@RecSys 2016: 29-34 - [i6]Hanjun Dai, Bo Dai, Le Song:
Discriminative Embeddings of Latent Variable Models for Structured Data. CoRR abs/1603.05629 (2016) - [i5]Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song:
Recurrent Coevolutionary Feature Embedding Processes for Recommendation. CoRR abs/1609.03675 (2016) - 2015
- [j1]Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Hanjun Dai, Tie-Yan Liu:
KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge. ACM Trans. Inf. Syst. 34(1): 4:1-4:25 (2015) - [c3]Shuang Li, Yao Xie, Hanjun Dai, Le Song:
M-Statistic for Kernel Change-Point Detection. NIPS 2015: 3366-3374 - [i4]Bo Dai, Niao He, Hanjun Dai, Le Song:
Scalable Bayesian Inference via Particle Mirror Descent. CoRR abs/1506.03101 (2015) - [i3]Shuang Li, Yao Xie, Hanjun Dai, Le Song:
M-Statistic for Kernel Change-Point Detection. CoRR abs/1507.01279 (2015) - [i2]Yao Xie, Ruiyang Song, Hanjun Dai, Qingbin Li, Le Song:
Online Supervised Subspace Tracking. CoRR abs/1509.00137 (2015) - 2014
- [c2]Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu:
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. AAAI 2014: 1369-1375 - [c1]Fei Tian, Hanjun Dai, Jiang Bian, Bin Gao, Rui Zhang, Enhong Chen, Tie-Yan Liu:
A Probabilistic Model for Learning Multi-Prototype Word Embeddings. COLING 2014: 151-160 - [i1]Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu:
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. CoRR abs/1404.5772 (2014)
Coauthor Index
[c65] [i59] [i58] [i55] [i53] [i52] [c62] [c61] [c57] [c56] [c55] [c52] [i50] [i40] [c47] [c43] [c42] [i37] [i33] [i32] [c36] [c35] [c34] [i30] [i29] [i28] [c29] [c28] [c27] [c26] [i27] [i26] [i25] [i23] [i21] [i19] [c25] [c20] [c19] [i17] [i16] [c16] [c15] [c13] [c11] [i14] [i11] [c10] [c7] [c6] [i6] [i4]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-02-08 00:54 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: