Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning. - Mohammad Taha Bahadori; Yan Liu 002; Dan Zhang 007 (ICDM, 2011)

On Causality Inference in Time Series. - Mohammad Taha Bahadori; Yan Liu 002 (AAAI Fall Symposium: Discovery Informatics, 2012)

Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling. - Yan Liu 002; Mohammad Taha Bahadori; Hongfei Li (ICML, 2012)

Granger Causality Analysis in Irregular Time Series. - Mohammad Taha Bahadori; Yan Liu 002 (SDM, 2012)

Fast structure learning in generalized stochastic processes with latent factors. - Mohammad Taha Bahadori; Yan Liu 002; Eric P. Xing (KDD, 2013)

An Examination of Practical Granger Causality Inference. - Yan Liu 002; Mohammad Taha Bahadori (SDM, 2013)

Scalable Heterogeneous Transfer Ranking. - Mohammad Taha Bahadori; Yi Chang 001; Bo Long; Yan Liu 002 (BigMine, 2014)

FBLG: a simple and effective approach for temporal dependence discovery from time series data. - Dehua Cheng; Mohammad Taha Bahadori; Yan Liu 002 (KDD, 2014)

Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning. - Mohammad Taha Bahadori; Qi Rose Yu; Yan Liu 002 (NIPS, 2014)

A general framework for scalable transductive transfer learning. - Mohammad Taha Bahadori; Yan Liu 002; Dan Zhang 007 (Knowl. Inf. Syst., 2014)

Causal Phenotype Discovery via Deep Networks. - David C. Kale; Zhengping Che; Mohammad Taha Bahadori; Wenzhe Li; Yan Liu 002; Randall C. Wetzel (AMIA, 2015)

Functional Subspace Clustering with Application to Time Series. - Mohammad Taha Bahadori; David C. Kale; Yingying Fan; Yan Liu 002 (ICML, 2015)

Deep Computational Phenotyping. - Zhengping Che; David C. Kale; Wenzhe Li; Mohammad Taha Bahadori; Yan Liu 002 (KDD, 2015)

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. - Edward Choi; Mohammad Taha Bahadori; Jimeng Sun 001 (CoRR, 2015)

Multi-layer Representation Learning for Medical Concepts. - Edward Choi; Mohammad Taha Bahadori; Elizabeth Searles; Catherine Coffey; Michael Thompson; James Bost; Javier Tejedor-Sojo; Jimeng Sun 001 (KDD, 2016)

FLASH: Fast Bayesian Optimization for Data Analytic Pipelines. - Yuyu Zhang; Mohammad Taha Bahadori; Hang Su; Jimeng Sun 001 (KDD, 2016)

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. - Edward Choi; Mohammad Taha Bahadori; Andy Schuetz; Walter F. Stewart; Jimeng Sun 001 (MLHC, 2016)

RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. - Edward Choi; Mohammad Taha Bahadori; Jimeng Sun 001; Joshua Kulas; Andy Schuetz; Walter F. Stewart (NIPS, 2016)

Multi-layer Representation Learning for Medical Concepts. - Edward Choi; Mohammad Taha Bahadori; Elizabeth Searles; Catherine Coffey; Jimeng Sun 001 (CoRR, 2016)

RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism. - Edward Choi; Mohammad Taha Bahadori; Andy Schuetz; Walter F. Stewart; Jimeng Sun 001 (CoRR, 2016)

Time Series Feature Learning with Applications to Health Care. - Zhengping Che; Sanjay Purushotham; David C. Kale; Wenzhe Li; Mohammad Taha Bahadori; Robinder G. Khemani; Yan Liu 002 (Mobile Health - Sensors, Analytic Methods, and Applications, 2017)

Predicting Changes in Pediatric Medical Complexity using Large Longitudinal Health Records. - Yanbo Xu; Mohammad Taha Bahadori; Elizabeth Searles; Michael Thompson; Javier Tejedor-Sojo; Jimeng Sun 001 (AMIA, 2017)

GRAM: Graph-based Attention Model for Healthcare Representation Learning. - Edward Choi; Mohammad Taha Bahadori; Le Song; Walter F. Stewart; Jimeng Sun 001 (KDD, 2017)

Causal Regularization. - Mohammad Taha Bahadori; Krzysztof Chalupka; Edward Choi; Robert Chen 001; Walter F. Stewart; Jimeng Sun 001 (CoRR, 2017)

Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning. - Mengqi Jin; Mohammad Taha Bahadori; Aaron Colak; Parminder Bhatia; Busra Celikkaya; Ram Bhakta; Selvan Senthivel; Mohammed Khalilia; Daniel Navarro; Borui Zhang; Tiberiu Doman; Arun Ravi; et al. (CoRR, 2018)

Temporal-Clustering Invariance in Irregular Healthcare Time Series. - Mohammad Taha Bahadori; Zachary Chase Lipton (CoRR, 2019)

Discovering Invariances in Healthcare Neural Networks. - Mohammad Taha Bahadori; Layne C. Price (CoRR, 2019)

Scalable Interpretable Multi-Response Regression via SEED. - Zemin Zheng; Mohammad Taha Bahadori; Yan Liu 002; Jinchi Lv (J. Mach. Learn. Res., 2019)

Debiasing Concept Bottleneck Models with Instrumental Variables. - Mohammad Taha Bahadori; David E. Heckerman (CoRR, 2020)

Debiasing Concept-based Explanations with Causal Analysis. - Mohammad Taha Bahadori; David Heckerman (ICLR, 2021)

End-to-End Balancing for Causal Continuous Treatment-Effect Estimation. - Mohammad Taha Bahadori; Eric Tchetgen Tchetgen; David Heckerman (ICML, 2022)

Multiply-Robust Causal Change Attribution. - Victor Quintas-Martinez; Mohammad Taha Bahadori; Eduardo Santiago; Jeff Mu; David Heckerman (ICML, 2024)

Multiply-Robust Causal Change Attribution. - Victor Quintas-Martinez; Mohammad Taha Bahadori; Eduardo Santiago; Jeff Mu; Dominik Janzing; David Heckerman (CoRR, 2024)

Fast Training Dataset Attribution via In-Context Learning. - Milad Fotouhi; Mohammad Taha Bahadori; Oluwaseyi Feyisetan; Payman Arabshahi; David Heckerman (CoRR, 2024)

Removing Spurious Correlation from Neural Network Interpretations. - Milad Fotouhi; Mohammad Taha Bahadori; Oluwaseyi Feyisetan; Payman Arabshahi; David Heckerman (CoRR, 2024)