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Energy-based View of Retrosynthesis

Jul 14, 2020
Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai


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

Jun 29, 2020
Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

* Presented at ICML 2020 

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Scalable Deep Generative Modeling for Sparse Graphs

Jun 28, 2020
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans

* ICML 2020 

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Learning to Stop While Learning to Predict

Jun 09, 2020
Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song

* Proceedings of the 37th International Conference on Machine Learning 

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Energy-Based Processes for Exchangeable Data

Mar 17, 2020
Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans


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Differentiable Top-k Operator with Optimal Transport

Feb 18, 2020
Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister


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Retrosynthesis Prediction with Conditional Graph Logic Network

Jan 06, 2020
Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song

* NeurIPS 2019 

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Learning Transferable Graph Exploration

Oct 28, 2019
Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli

* To appear in NeurIPS 2019 

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

Jun 01, 2019
Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru


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Exponential Family Estimation via Adversarial Dynamics Embedding

Apr 27, 2019
Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

* 66 figures, 25 pages; preliminary version published in NeurIPS2018 Bayesian Deep Learning Workshop 

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Meta Particle Flow for Sequential Bayesian Inference

Feb 02, 2019
Xinshi Chen, Hanjun Dai, Le Song


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

Dec 04, 2018
Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Edward Grefenstette, Pushmeet Kohli, Peter Battaglia

* Presented at the Learning by Instruction (LBI) Workshop at NeurIPS 2018 

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Kernel Exponential Family Estimation via Doubly Dual Embedding

Nov 06, 2018
Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He

* 22 pages, 20 figures 

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Adversarial Attack on Graph Structured Data

Jun 06, 2018
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song

* to appear in ICML 2018 

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

May 31, 2018
Yuyu Zhang, Hanjun Dai, Kamil Toraman, Le Song


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Syntax-Directed Variational Autoencoder for Structured Data

Feb 24, 2018
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song

* to appear in ICLR 2018 

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Learning Combinatorial Optimization Algorithms over Graphs

Feb 21, 2018
Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song

* NIPS 2017 

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Variational Reasoning for Question Answering with Knowledge Graph

Nov 27, 2017
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song


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Scan $B$-Statistic for Kernel Change-Point Detection

Sep 24, 2017
Shuang Li, Yao Xie, Hanjun Dai, Le Song

* Submitted for journal publication. Partial results appeared in NIPS 2015 

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Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

Jun 21, 2017
Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song


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Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

Feb 28, 2017
Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song

* Recsys Workshop on Deep Learning for Recommendation Systems (DLRS '16) 

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Discriminative Embeddings of Latent Variable Models for Structured Data

Sep 26, 2016
Hanjun Dai, Bo Dai, Le Song

* ICML 2016 

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Provable Bayesian Inference via Particle Mirror Descent

May 05, 2016
Bo Dai, Niao He, Hanjun Dai, Le Song

* 38 pages, 26 figures 

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Online Supervised Subspace Tracking

Sep 01, 2015
Yao Xie, Ruiyang Song, Hanjun Dai, Qingbin Li, Le Song

* Submitted for journal publication 

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Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks

Jul 28, 2014
Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu

* Accepted by AAAI 2014 

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