Mohammad Taha Bahadori

According to our database, Mohammad Taha Bahadori authored at least 34 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Fast Training Dataset Attribution via In-Context Learning.

Milad Fotouhi, Mohammad Taha Bahadori, Oluwaseyi Feyisetan, Payman Arabshahi, David Heckerman

CoRR, 2024

Multiply-Robust Causal Change Attribution.

Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman

CoRR, 2024

Multiply-Robust Causal Change Attribution.

Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, David Heckerman

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2022

End-to-End Balancing for Causal Continuous Treatment-Effect Estimation.

Mohammad Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman

Proceedings of the International Conference on Machine Learning, 2022

2021

Debiasing Concept-based Explanations with Causal Analysis.

Mohammad Taha Bahadori, David Heckerman

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Debiasing Concept Bottleneck Models with Instrumental Variables.

Mohammad Taha Bahadori, David E. Heckerman

CoRR, 2020

2019

Scalable Interpretable Multi-Response Regression via SEED.

Zemin Zheng, Mohammad Taha Bahadori, Yan Liu, Jinchi Lv

J. Mach. Learn. Res., 2019

Discovering Invariances in Healthcare Neural Networks.

Mohammad Taha Bahadori, Layne C. Price

CoRR, 2019

Temporal-Clustering Invariance in Irregular Healthcare Time Series.

Mohammad Taha Bahadori, Zachary Chase Lipton

CoRR, 2019

2018

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, Matthieu Liger, Taha A. Kass-Hout

CoRR, 2018

2017

Causal Regularization.

Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi, Robert Chen, Walter F. Stewart, Jimeng Sun

CoRR, 2017

GRAM: Graph-based Attention Model for Healthcare Representation Learning.

Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 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

Proceedings of the AMIA 2017, 2017

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

Proceedings of the Mobile Health - Sensors, Analytic Methods, and Applications, 2017

2016

RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism.

Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, Jimeng Sun

CoRR, 2016

Multi-layer Representation Learning for Medical Concepts.

Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, Jimeng Sun

CoRR, 2016

RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism.

Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, Walter F. Stewart

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks.

Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, Jimeng Sun

Proceedings of the 1st Machine Learning in Health Care, 2016

FLASH: Fast Bayesian Optimization for Data Analytic Pipelines.

Yuyu Zhang, Mohammad Taha Bahadori, Hang Su, Jimeng Sun

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

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

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks.

Edward Choi, Mohammad Taha Bahadori, Jimeng Sun

CoRR, 2015

Deep Computational Phenotyping.

Zhengping Che, David C. Kale, Wenzhe Li, Mohammad Taha Bahadori, Yan Liu

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Functional Subspace Clustering with Application to Time Series.

Mohammad Taha Bahadori, David C. Kale, Yingying Fan, Yan Liu

Proceedings of the 32nd International Conference on Machine Learning, 2015

Causal Phenotype Discovery via Deep Networks.

David C. Kale, Zhengping Che, Mohammad Taha Bahadori, Wenzhe Li, Yan Liu, Randall C. Wetzel

Proceedings of the AMIA 2015, 2015

2014

A general framework for scalable transductive transfer learning.

Mohammad Taha Bahadori, Yan Liu, Dan Zhang

Knowl. Inf. Syst., 2014

Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning.

Mohammad Taha Bahadori, Qi Rose Yu, Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

FBLG: a simple and effective approach for temporal dependence discovery from time series data.

Dehua Cheng, Mohammad Taha Bahadori, Yan Liu

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Scalable Heterogeneous Transfer Ranking.

Mohammad Taha Bahadori, Yi Chang, Bo Long, Yan Liu

Proceedings of the 3rd International Workshop on Big Data, 2014

2013

An Examination of Practical Granger Causality Inference.

Yan Liu, Mohammad Taha Bahadori

Proceedings of the 13th SIAM International Conference on Data Mining, 2013

Fast structure learning in generalized stochastic processes with latent factors.

Mohammad Taha Bahadori, Yan Liu, Eric P. Xing

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

2012

Granger Causality Analysis in Irregular Time Series.

Mohammad Taha Bahadori, Yan Liu

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling.

Yan Liu, Mohammad Taha Bahadori, Hongfei Li

Proceedings of the 29th International Conference on Machine Learning, 2012

On Causality Inference in Time Series.

Mohammad Taha Bahadori, Yan Liu

Proceedings of the Discovery Informatics: The Role of AI Research in Innovating Scientific Processes, 2012

2011

Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning.

Mohammad Taha Bahadori, Yan Liu, Dan Zhang

Proceedings of the 11th IEEE International Conference on Data Mining, 2011