Rajesh Ranganath

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 117

Top Venues

Publications

Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning. CoRR (2024)
Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. Trans. Mach. Learn. Res. 2024 (2024)
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions. CoRR (2024)
Aahlad Manas Puli, Nitish Joshi, Yoav Wald, He He, Rajesh Ranganath
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. Trans. Mach. Learn. Res. 2024 (2024)
Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
On the Challenges and Opportunities in Generative AI. CoRR (2024)
Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi M. Schambra, Carlos Fernandez-Granda
Quantifying impairment and disease severity using AI models trained on healthy subjects. npj Digit. Medicine 7 (1) (2024)
Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath
From algorithms to action: improving patient care requires causality. BMC Medical Informatics Decis. Mak. 24 (3) (2024)
Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. CoRR (2024)
Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, Kyunghyun Cho
Preference Learning Algorithms Do Not Learn Preference Rankings. CoRR (2024)
Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit Chopra, Lerrel Pinto
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction. CoRR (2024)
Yuxuan Hu, Albert Lui, Mark Goldstein, Mukund Sudarshan, Andrea Tinsay, Cindy Tsui, Samuel Maidman, John Medamana, Neil Jethani, Aahlad Manas Puli, Vuthy Nguy, Yindalon Aphinyanaphongs, Nicholas Kiefer, Nathaniel Smilowitz, James Horowitz, Tania Ahuja, Glenn I Fishman, Judith Hochman, Stuart Katz, Samuel Bernard, Rajesh Ranganath
A dynamic risk score for early prediction of cardiogenic shock using machine learning. CoRR (2023)
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. CoRR (2023)
Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich
Shortcut Learning Through the Lens of Early Training Dynamics. CoRR (2023)
Aahlad Manas Puli, Lily H. Zhang, Yoav Wald, Rajesh Ranganath
Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy. NeurIPS (2023)
Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, Giovanni Ciná
When accurate prediction models yield harmful self-fulfilling prophecies. CoRR (2023)
Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi M. Schambra, Carlos Fernandez-Granda
Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects. CoRR (2023)
Raghav Singhal, Mukund Sudarshan, Anish Mahishi, Sri Kaushik, Luke Ginocchio, Angela Tong, Hersh Chandarana, Daniel K. Sodickson, Rajesh Ranganath, Sumit Chopra
On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease. CoRR (2023)
Rhys Compton, Lily H. Zhang, Aahlad Manas Puli, Rajesh Ranganath
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. CoRR (2023)
Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner
An Effective Meaningful Way to Evaluate Survival Models. CoRR (2023)
Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics. Trans. Mach. Learn. Res. 2023 (2023)
Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
Stochastic interpolants with data-dependent couplings. CoRR (2023)
Lily H. Zhang, Rajesh Ranganath
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection. AAAI (2023)
Neil Jethani, Adriel Saporta, Rajesh Ranganath
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. AISTATS (2023)
Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath
DIET: Conditional independence testing with marginal dependence measures of residual information. AISTATS (2023)
Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner
An Effective Meaningful Way to Evaluate Survival Models. ICML (2023)
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. ICLR (2023)
Neil Jethani, Adriel Saporta, Rajesh Ranganath
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. CoRR (2023)
Rhys Compton, Lily H. Zhang, Aahlad Manas Puli, Rajesh Ranganath
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. MLHC (2023)
Lily H. Zhang, Rajesh Ranganath
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection. CoRR (2023)
Aahlad Manas Puli, Lily H. Zhang, Yoav Wald, Rajesh Ranganath
Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy. CoRR (2023)
Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. ICML (2022)
Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath
Decision making in cancer: Causal questions require causal answers. CoRR (2022)
Aahlad Manas Puli, Nitish Joshi, He He, Rajesh Ranganath
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. CoRR (2022)
Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. CoRR (2022)
Xintian Han, Mark Goldstein, Rajesh Ranganath
Survival Mixture Density Networks. CoRR (2022)
Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller
Learning Invariant Representations with Missing Data. CLeaR (2022)
Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath
DIET: Conditional independence testing with marginal dependence measures of residual information. CoRR (2022)
Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath
FastSHAP: Real-Time Shapley Value Estimation. ICLR (2022)
Neil Jethani, Aahlad Manas Puli, Hao Zhang, Leonid Garber, Lior Jankelson, Yindalon Aphinyanaphongs, Rajesh Ranganath
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography. CoRR (2022)
Xintian Han, Mark Goldstein, Rajesh Ranganath
Survival Mixture Density Networks. MLHC (2022)
Aahlad Manas Puli, Lily H. Zhang, Eric Karl Oermann, Rajesh Ranganath
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations. ICLR (2022)
Aahlad Manas Puli, Lily H. Zhang, Eric K. Oermann, Rajesh Ranganath
Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations. CoRR (2021)
David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna
Quantile Filtered Imitation Learning. CoRR (2021)
Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath
CONTRA: Contrarian statistics for controlled variable selection. AISTATS (2021)
Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath
Causal Estimation with Functional Confounders. CoRR (2021)
Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath
X-CAL: Explicit Calibration for Survival Analysis. CoRR (2021)
David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna
Offline RL Without Off-Policy Evaluation. CoRR (2021)
David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna
Offline Contextual Bandits with Overparameterized Models. ICML (2021)
Lily H. Zhang, Mark Goldstein, Rajesh Ranganath
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. CoRR (2021)
Lily H. Zhang, Mark Goldstein, Rajesh Ranganath
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. ICML (2021)
Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller
Learning Invariant Representations with Missing Data. CoRR (2021)
David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna
Offline RL Without Off-Policy Evaluation. NeurIPS (2021)
Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath
Inverse-Weighted Survival Games. NeurIPS (2021)
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. AISTATS (2021)
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. CoRR (2021)
Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath
Inverse-Weighted Survival Games. CoRR (2021)
Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath
FastSHAP: Real-Time Shapley Value Estimation. CoRR (2021)
David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna
Overfitting and Optimization in Offline Policy Learning. CoRR (2020)
Narges Razavian, Vincent J. Major, Mukund Sudarshan, Jesse Burk-Rafel, Peter Stella, Hardev Randhawa, Seda Bilaloglu, Ji Chen, Vuthy Nguy, Walter Wang, Hao Zhang, Ilan Reinstein, David Kudlowitz, Cameron Zenger, Meng Cao, Ruina Zhang, Siddhant Dogra, Keerthi B. Harish, Brian Bosworth, Fritz Francois, Leora I. Horwitz, Rajesh Ranganath, Jonathan Austrian, Yindalon Aphinyanaphongs
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients. npj Digit. Medicine 3 (2020)
Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte
Adversarially-Learned Balancing Weights for Causal Inference. AMIA (2020)
Shreyas Bhave, Xintian Han, Rajesh Ranganath, Adler J. Perotte
Deep Survival Analysis: The Impact of Feature Missingness. AMIA (2020)
Aahlad Manas Puli, Rajesh Ranganath
General Control Functions for Causal Effect Estimation from IVs. NeurIPS (2020)
Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath
Causal Estimation with Functional Confounders. NeurIPS (2020)
Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte
The Counterfactual χ-GAN: Finding comparable cohorts in observational health data. J. Biomed. Informatics 109 (2020)
Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Deep Direct Likelihood Knockoffs. NeurIPS (2020)
Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath
X-CAL: Explicit Calibration for Survival Analysis. NeurIPS (2020)
Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Deep Direct Likelihood Knockoffs. CoRR (2020)
Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath
Probabilistic Machine Learning for Healthcare. CoRR (2020)
Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte
The Counterfactual χ-GAN. CoRR (2020)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS (2019)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR (2019)
Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini
Reproducibility in Machine Learning for Health. CoRR (2019)
Raghav Singhal, Saad Lahlou, Rajesh Ranganath
Kernelized Complete Conditional Stein Discrepancy. CoRR (2019)
Da Tang, Rajesh Ranganath
The Variational Predictive Natural Gradient. ICML (2019)
Xintian Han, Yuxuan Hu, Luca Foschini, Larry Chinitz, Lior Jankelson, Rajesh Ranganath
Adversarial Examples for Electrocardiograms. CoRR (2019)
Da Tang, Rajesh Ranganath
The Variational Predictive Natural Gradient. CoRR (2019)
Rajesh Ranganath, David M. Blei
Population Predictive Checks. CoRR (2019)
Zenna Tavares, Xin Zhang, Edgar Minaysan, Javier Burroni, Rajesh Ranganath, Armando Solar-Lezama
The Random Conditional Distribution for Higher-Order Probabilistic Inference. CoRR (2019)
Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath
Support and Invertibility in Domain-Invariant Representations. CoRR (2019)
Aahlad Manas Puli, Rajesh Ranganath
Generalized Control Functions via Variational Decoupling. CoRR (2019)
Zenna Tavares, Javier Burroni, Edgar Minaysan, Armando Solar-Lezama, Rajesh Ranganath
Soft Constraints for Inference with Declarative Knowledge. CoRR (2019)
Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath
Support and Invertibility in Domain-Invariant Representations. AISTATS (2019)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR (2019)
Kexin Huang, Jaan Altosaar, Rajesh Ranganath
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission. CoRR (2019)
Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini
Reproducibility in Machine Learning for Health. RML@ICLR (2019)
Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath
Predicate Exchange: Inference with Declarative Knowledge. ICML (2019)
Jeremy R. Manning, Xia Zhu, Theodore L. Willke, Rajesh Ranganath, Kimberly L. Stachenfeld, Uri Hasson, David M. Blei, Kenneth A. Norman
A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage 180 (Part) (2018)
Rajesh Ranganath, Adler J. Perotte
Multiple Causal Inference with Latent Confounding. CoRR (2018)
Jaan Altosaar, Rajesh Ranganath, David M. Blei
Proximity Variational Inference. AISTATS (2018)
Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
Noisin: Unbiased Regularization for Recurrent Neural Networks. CoRR (2018)
Xenia Miscouridou, Adler J. Perotte, Noemie Elhadad, Rajesh Ranganath
Deep Survival Analysis: Nonparametrics and Missingness. MLHC (2018)
Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag
Max-margin learning with the Bayes factor. UAI (2018)
Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
Noisin: Unbiased Regularization for Recurrent Neural Networks. ICML (2018)
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath
Opportunities in Machine Learning for Healthcare. CoRR (2018)
Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
Variational Sequential Monte Carlo. AISTATS (2018)
Jaan Altosaar, Rajesh Ranganath, David M. Blei
Proximity Variational Inference. CoRR (2017)
Dustin Tran, Rajesh Ranganath, David M. Blei
Deep and Hierarchical Implicit Models. CoRR (2017)
Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
Variational Inference via \chi Upper Bound Minimization. NIPS (2017)
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
Automatic Differentiation Variational Inference. J. Mach. Learn. Res. 18 (2017)
Dustin Tran, Rajesh Ranganath, David M. Blei
Hierarchical Implicit Models and Likelihood-Free Variational Inference. NIPS (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Rajesh Ranganath