Learning Networks of Heterogeneous Influence. - Nan Du 002; Le Song; Alexander J. Smola; Ming Yuan 001 (NIPS, 2012)

Uncover Topic-Sensitive Information Diffusion Networks. - Nan Du 002; Le Song; Hyenkyun Woo; Hongyuan Zha (AISTATS, 2013)

Scalable Influence Estimation in Continuous-Time Diffusion Networks. - Nan Du 002; Le Song; Manuel Gomez-Rodriguez; Hongyuan Zha (NIPS, 2013)

Continuous-Time Influence Maximization for Multiple Items. - Nan Du 002; Yingyu Liang; Maria-Florina Balcan; Le Song (CoRR, 2013)

Influence Function Learning in Information Diffusion Networks. - Nan Du 002; Yingyu Liang; Maria-Florina Balcan; Le Song (ICML, 2014)

Learning Time-Varying Coverage Functions. - Nan Du 002; Yingyu Liang; Maria-Florina Balcan; Le Song (NIPS, 2014)

Shaping Social Activity by Incentivizing Users. - Mehrdad Farajtabar; Nan Du 002; Manuel Gomez-Rodriguez; Isabel Valera; Hongyuan Zha; Le Song (NIPS, 2014)

Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades. - Mehrdad Farajtabar; Manuel Gomez-Rodriguez; Mohammad Zamani; Nan Du 002; Hongyuan Zha; Le Song (AISTATS, 2015)

Constructing Disease Network and Temporal Progression Model via Context-Sensitive Hawkes Process. - Edward Choi 003; Nan Du 002; Robert Chen 001; Le Song; Jimeng Sun 001 (ICDM, 2015)

Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams. - Nan Du 002; Mehrdad Farajtabar; Amr Ahmed 001; Alexander J. Smola; Le Song (KDD, 2015)

Time-Sensitive Recommendation From Recurrent User Activities. - Nan Du 002; Yichen Wang 001; Niao He; Jimeng Sun 001; Le Song (NIPS, 2015)

Isotonic Hawkes Processes. - Yichen Wang 001; Bo Xie 002; Nan Du 002; 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)

Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions. - Yichen Wang 001; Nan Du 002; Rakshit Trivedi; Le Song (NIPS, 2016)

Influence Estimation and Maximization in Continuous-Time Diffusion Networks. - Manuel Gomez-Rodriguez; Le Song; Nan Du 002; Hongyuan Zha; Bernhard Schölkopf (ACM Trans. Inf. Syst., 2016)

Modeling, learning, and inference of high-dimensional asynchronous event data. - Nan Du 002 (2016)

Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks. - Nan Du 002; Yingyu Liang; Maria-Florina Balcan; Manuel Gomez-Rodriguez; Hongyuan Zha; Le Song (J. Mach. Learn. Res., 2017)

Learning Temporal Point Processes via Reinforcement Learning. - Shuang Li 002; Shuai Xiao; Shixiang Zhu; Nan Du 002; Yao Xie 002; Le Song (NeurIPS, 2018)

Extracting Symptoms and their Status from Clinical Conversations. - Nan Du 002; Kai Chen 010; Anjuli Kannan; Linh Tran; Yuhui Chen; Izhak Shafran (ACL (1), 2019)

Learning to Infer Entities, Properties and their Relations from Clinical Conversations. - Nan Du 002; Mingqiu Wang; Linh Tran; Gang Lee; Izhak Shafran (EMNLP/IJCNLP (1), 2019)

Learning to Infer Entities, Properties and their Relations from Clinical Conversations. - Nan Du 002; Mingqiu Wang; Linh Tran; Gang Li; Izhak Shafran (CoRR, 2019)

Deep Physiological State Space Model for Clinical Forecasting. - Yuan Xue; Denny Zhou; Nan Du 002; Andrew M. Dai; Zhen Xu 006; Kun Zhang 043; Claire Cui (CoRR, 2019)

Deep State-Space Generative Model For Correlated Time-to-Event Predictions. - Yuan Xue; Denny Zhou; Nan Du 002; Andrew M. Dai; Zhen Xu 006; Kun Zhang 043; Claire Cui (KDD, 2020)

The Medical Scribe: Corpus Development and Model Performance Analyses. - Izhak Shafran; Nan Du 002; Linh Tran; Amanda Perry; Lauren Keyes; Mark Knichel; Ashley Domin; Lei Huang; Yuhui Chen; Gang Li; Mingqiu Wang; Laurent El Shafey; et al. (LREC, 2020)

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction. - Yuan Xue; Nan Du 002; Anne Mottram; Martin Seneviratne; Andrew M. Dai (NeurIPS, 2020)

R2D2: Relational Text Decoding with Transformers. - Aryan Arbabi; Mingqiu Wang; Laurent El Shafey; Nan Du 002; Izhak Shafran (CoRR, 2021)

Finetuned Language Models are Zero-Shot Learners. - Jason Wei; Maarten Bosma; Vincent Y. Zhao; Kelvin Guu; Adams Wei Yu; Brian Lester; Nan Du 002; Andrew M. Dai; Quoc V. Le (ICLR, 2022)

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. - Nan Du 002; Yanping Huang; Andrew M. Dai; Simon Tong; Dmitry Lepikhin; Yuanzhong Xu; Maxim Krikun; Yanqi Zhou; Adams Wei Yu; Orhan Firat; Barret Zoph; Liam Fedus; et al. (ICML, 2022)

Mixture-of-Experts with Expert Choice Routing. - Yanqi Zhou; Tao Lei; Hanxiao Liu; Nan Du 002; Yanping Huang; Vincent Y. Zhao; Andrew M. Dai; Zhifeng Chen; Quoc V. Le; James Laudon (NeurIPS, 2022)

Designing Effective Sparse Expert Models. - Barret Zoph; Irwan Bello; Sameer Kumar; Nan Du 002; Yanping Huang; Jeff Dean; Noam Shazeer; William Fedus (CoRR, 2022)

Massively Multilingual Shallow Fusion with Large Language Models. - Ke Hu; Tara N. Sainath; Bo Li 028; Nan Du 002; Yanping Huang; Andrew M. Dai; Yu Zhang 033; Rodrigo Cabrera; Zhifeng Chen; Trevor Strohman (ICASSP, 2023)

ReAct: Synergizing Reasoning and Acting in Language Models. - Shunyu Yao; Jeffrey Zhao; Dian Yu; Nan Du 002; Izhak Shafran; Karthik R. Narasimhan; Yuan Cao 007 (ICLR, 2023)

Lifelong Language Pretraining with Distribution-Specialized Experts. - Wuyang Chen 001; Yanqi Zhou; Nan Du 002; Yanping Huang; James Laudon; Zhifeng Chen; Claire Cui (ICML, 2023)

Brainformers: Trading Simplicity for Efficiency. - Yanqi Zhou; Nan Du 002; Yanping Huang; Daiyi Peng; Chang Lan; Da Huang; Siamak Shakeri; David R. So; Andrew M. Dai; Yifeng Lu; Zhifeng Chen; Quoc V. Le; et al. (ICML, 2023)

Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference. - Tao Lei; Junwen Bai; Siddhartha Brahma; Joshua Ainslie; Kenton Lee; Yanqi Zhou; Nan Du 002; Vincent Y. Zhao; Yuexin Wu; Bo Li 028; Yu Zhang 033; Ming-Wei Chang (NeurIPS, 2023)

DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining. - Sang Michael Xie; Hieu Pham 001; Xuanyi Dong; Nan Du 002; Hanxiao Liu; Yifeng Lu; Percy Liang; Quoc V. Le; Tengyu Ma 001; Adams Wei Yu (NeurIPS, 2023)

PaLM 2 Technical Report. - Rohan Anil; Andrew M. Dai; Orhan Firat; Melvin Johnson; Dmitry Lepikhin; Alexandre Passos; Siamak Shakeri; Emanuel Taropa; Paige Bailey; Zhifeng Chen; Eric Chu; Jonathan H. Clark; et al. (CoRR, 2023)

Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts. - Sheng Shen 001; Le Hou; Yanqi Zhou; Nan Du 002; Shayne Longpre; Jason Wei; Hyung Won Chung; Barret Zoph; William Fedus; Xinyun Chen; Tu Vu; Yuexin Wu; et al. (CoRR, 2023)

Learning to Skip for Language Modeling. - Dewen Zeng; Nan Du 002; Tao Wang; Yuanzhong Xu; Tao Lei; Zhifeng Chen; Claire Cui (CoRR, 2023)

PaLM: Scaling Language Modeling with Pathways. - Aakanksha Chowdhery; Sharan Narang; Jacob Devlin; Maarten Bosma; Gaurav Mishra; Adam Roberts; Paul Barham 001; Hyung Won Chung; Charles Sutton; Sebastian Gehrmann; Parker Schuh; Kensen Shi; et al. (J. Mach. Learn. Res., 2023)

Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models. - Sheng Shen 001; Le Hou; Yanqi Zhou; Nan Du 002; Shayne Longpre; Jason Wei; Hyung Won Chung; Barret Zoph; William Fedus; Xinyun Chen; Tu Vu; Yuexin Wu; et al. (ICLR, 2024)

Knowledge Graph Reasoning with Self-supervised Reinforcement Learning. - Ying Ma; Owen Burns; Mingqiu Wang; Gang Li; Nan Du 002; Laurent El Shafey; Liqiang Wang; Izhak Shafran; Hagen Soltau (CoRR, 2024)

Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction. - Yuan Xue; Nan Du 002; Anne Mottram; Martin Seneviratne; Andrew M. Dai (CoRR, 2024)