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Andrew M. Dai

BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark


Jul 17, 2021
Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai

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MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records


Feb 03, 2021
Zhen Xu, David R. So, Andrew M. Dai

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* Accepted for publication at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) 
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Learnability and Complexity of Quantum Samples


Oct 22, 2020
Murphy Yuezhen Niu, Andrew M. Dai, Li Li, Augustus Odena, Zhengli Zhao, Vadim Smelyanskyi, Hartmut Neven, Sergio Boixo

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Training independent subnetworks for robust prediction


Oct 13, 2020
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran

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Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss


Jul 10, 2020
Kamil Nar, Yuan Xue, Andrew M. Dai

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Flow Contrastive Estimation of Energy-Based Models


Dec 02, 2019
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu

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Modelling EHR timeseries by restricting feature interaction


Nov 14, 2019
Kun Zhang, Yuan Xue, Gerardo Flores, Alvin Rajkomar, Claire Cui, Andrew M. Dai

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* Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract 
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Federated and Differentially Private Learning for Electronic Health Records


Nov 13, 2019
Stephen R. Pfohl, Andrew M. Dai, Katherine Heller

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* Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract 
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Capacity, Bandwidth, and Compositionality in Emergent Language Learning


Oct 24, 2019
Cinjon Resnick, Abhinav Gupta, Jakob Foerster, Andrew M. Dai, Kyunghyun Cho

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* The first two authors contributed equally 
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Improved Patient Classification with Language Model Pretraining Over Clinical Notes


Oct 02, 2019
Jonas Kemp, Alvin Rajkomar, Andrew M. Dai

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* Accepted at NeurIPS ML4H 2019, extended abstract track 
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Learning an Adaptive Learning Rate Schedule


Sep 20, 2019
Zhen Xu, Andrew M. Dai, Jonas Kemp, Luke Metz

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Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records


Jun 28, 2019
Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai

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Analyzing the Role of Model Uncertainty for Electronic Health Records


Jun 10, 2019
Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai

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* Presented at the ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning. Code to be open-sourced 
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Gmail Smart Compose: Real-Time Assisted Writing


May 17, 2019
Mia Xu Chen, Benjamin N Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy Sohn, Yonghui Wu

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Music Transformer


Oct 10, 2018
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck

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* Rewrote many sections to clarify the work, and extended relative attention to the local case. Previous title is "An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation" 
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Peptide-Spectra Matching from Weak Supervision


Aug 22, 2018
Samuel S. Schoenholz, Sean Hackett, Laura Deming, Eugene Melamud, Navdeep Jaitly, Fiona McAllister, Jonathon O'Brien, George Dahl, Bryson Bennett, Andrew M. Dai, Daphne Koller

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Learning Longer-term Dependencies in RNNs with Auxiliary Losses


Jun 13, 2018
Trieu H. Trinh, Andrew M. Dai, Minh-Thang Luong, Quoc V. Le

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* ICML 2018 
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Embedding Text in Hyperbolic Spaces


Jun 12, 2018
Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl

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* TextGraphs 2018 
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Scalable and accurate deep learning for electronic health records


May 11, 2018
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Peter J. Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E. Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael Howell, Claire Cui, Greg Corrado, Jeff Dean

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* npj Digital Medicine 1:18 (2018) 
 * Published version from https://www.nature.com/articles/s41746-018-0029-1 
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MaskGAN: Better Text Generation via Filling in the______


Mar 01, 2018
William Fedus, Ian Goodfellow, Andrew M. Dai

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* 16 pages, ICLR 2018 
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Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step


Feb 20, 2018
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

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* 18 pages 
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Who Said What: Modeling Individual Labelers Improves Classification


Jan 04, 2018
Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton

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* AAAI 2018 
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Adversarial Training Methods for Semi-Supervised Text Classification


May 06, 2017
Takeru Miyato, Andrew M. Dai, Ian Goodfellow

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* Published as a conference paper at ICLR 2017 
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Generating Sentences from a Continuous Space


May 12, 2016
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, Samy Bengio

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* SIGNLL Conference on Computational Natural Language Learning (CONLL), 2016 
 * First two authors contributed equally. Work was done when all authors were at Google, Inc 
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Semi-supervised Sequence Learning


Nov 04, 2015
Andrew M. Dai, Quoc V. Le

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Document Embedding with Paragraph Vectors


Jul 29, 2015
Andrew M. Dai, Christopher Olah, Quoc V. Le

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* 8 pages 
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The supervised hierarchical Dirichlet process


Dec 17, 2014
Andrew M. Dai, Amos J. Storkey

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* 14 pages 
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