Rajesh Ranganath

According to our database, Rajesh Ranganath authored at least 89 papers between 2009 and 2024.

Collaborative distances:

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

2024

On the Challenges and Opportunities in Generative AI.

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

CoRR, 2024

Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning.

Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran

CoRR, 2024

2023

When accurate prediction models yield harmful self-fulfilling prophecies.

Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, Giovanni Ciná

CoRR, 2023

Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects.

Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi M. Schambra, Carlos Fernandez-Granda

CoRR, 2023

Stochastic interpolants with data-dependent couplings.

Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden

CoRR, 2023

A dynamic risk score for early prediction of cardiogenic shock using machine learning.

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

CoRR, 2023

Shortcut Learning Through the Lens of Early Training Dynamics.

Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich

CoRR, 2023

On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease.

Raghav Singhal, Mukund Sudarshan, Anish Mahishi, Sri Kaushik, Luke Ginocchio, Angela Tong, Hersh Chandarana, Daniel K. Sodickson, Rajesh Ranganath, Sumit Chopra

CoRR, 2023

Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy.

Aahlad Manas Puli, Lily H. Zhang, Yoav Wald, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations.

Rhys Compton, Lily H. Zhang, Aahlad Manas Puli, Rajesh Ranganath

Proceedings of the Machine Learning for Healthcare Conference, 2023

An Effective Meaningful Way to Evaluate Survival Models.

Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner

Proceedings of the International Conference on Machine Learning, 2023

Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions.

Raghav Singhal, Mark Goldstein, Rajesh Ranganath

Proceedings of the Eleventh International Conference on Learning Representations, 2023

DIET: Conditional independence testing with marginal dependence measures of residual information.

Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation.

Neil Jethani, Adriel Saporta, Rajesh Ranganath

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection.

Lily H. Zhang, Rajesh Ranganath

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation.

Aahlad Manas Puli, Nitish Joshi, He He, Rajesh Ranganath

CoRR, 2022

Decision making in cancer: Causal questions require causal answers.

Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath

CoRR, 2022

New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography.

Neil Jethani, Aahlad Manas Puli, Hao Zhang, Leonid Garber, Lior Jankelson, Yindalon Aphinyanaphongs, Rajesh Ranganath

CoRR, 2022

Survival Mixture Density Networks.

Xintian Han, Mark Goldstein, Rajesh Ranganath

Proceedings of the Machine Learning for Healthcare Conference, 2022

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets.

Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath

Proceedings of the International Conference on Machine Learning, 2022

Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations.

Aahlad Manas Puli, Lily H. Zhang, Eric Karl Oermann, Rajesh Ranganath

Proceedings of the Tenth International Conference on Learning Representations, 2022

FastSHAP: Real-Time Shapley Value Estimation.

Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Invariant Representations with Missing Data.

Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Quantile Filtered Imitation Learning.

David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna

CoRR, 2021

Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations.

Aahlad Manas Puli, Lily H. Zhang, Eric K. Oermann, Rajesh Ranganath

CoRR, 2021

Inverse-Weighted Survival Games.

Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Offline RL Without Off-Policy Evaluation.

David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models.

Lily H. Zhang, Mark Goldstein, Rajesh Ranganath

Proceedings of the 38th International Conference on Machine Learning, 2021

Offline Contextual Bandits with Overparameterized Models.

David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna

Proceedings of the 38th International Conference on Machine Learning, 2021

CONTRA: Contrarian statistics for controlled variable selection.

Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations.

Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients.

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 S. Austrian, Yindalon Aphinyanaphongs

npj Digit. Medicine, 2020

The Counterfactual χ-GAN: Finding comparable cohorts in observational health data.

Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte

J. Biomed. Informatics, 2020

Probabilistic Machine Learning for Healthcare.

Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath

CoRR, 2020

Overfitting and Optimization in Offline Policy Learning.

David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna

CoRR, 2020

The Counterfactual χ-GAN.

Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte

CoRR, 2020

Deep Direct Likelihood Knockoffs.

Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

General Control Functions for Causal Effect Estimation from IVs.

Aahlad Manas Puli, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Estimation with Functional Confounders.

Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

X-CAL: Explicit Calibration for Survival Analysis.

Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep Survival Analysis: The Impact of Feature Missingness.

Shreyas Bhave, Xintian Han, Rajesh Ranganath, Adler J. Perotte

Proceedings of the AMIA 2020, 2020

Adversarially-Learned Balancing Weights for Causal Inference.

Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte

Proceedings of the AMIA 2020, 2020

2019

Population Predictive Checks.

Rajesh Ranganath, David M. Blei

CoRR, 2019

Generalized Control Functions via Variational Decoupling.

Aahlad Manas Puli, Rajesh Ranganath

CoRR, 2019

Adversarial Examples for Electrocardiograms.

Xintian Han, Yuxuan Hu, Luca Foschini, Larry Chinitz, Lior Jankelson, Rajesh Ranganath

CoRR, 2019

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission.

Kexin Huang, Jaan Altosaar, Rajesh Ranganath

CoRR, 2019

Kernelized Complete Conditional Stein Discrepancy.

Raghav Singhal, Saad Lahlou, Rajesh Ranganath

CoRR, 2019

The Random Conditional Distribution for Higher-Order Probabilistic Inference.

Zenna Tavares, Xin Zhang, Edgar Minaysan, Javier Burroni, Rajesh Ranganath, Armando Solar-Lezama

CoRR, 2019

Soft Constraints for Inference with Declarative Knowledge.

Zenna Tavares, Javier Burroni, Edgar Minaysan, Armando Solar-Lezama, Rajesh Ranganath

CoRR, 2019

Energy-Inspired Models: Learning with Sampler-Induced Distributions.

Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Predicate Exchange: Inference with Declarative Knowledge.

Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath

Proceedings of the 36th International Conference on Machine Learning, 2019

The Variational Predictive Natural Gradient.

Da Tang, Rajesh Ranganath

Proceedings of the 36th International Conference on Machine Learning, 2019

Reproducibility in Machine Learning for Health.

Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini

Proceedings of the Reproducibility in Machine Learning, 2019

Revisiting Auxiliary Latent Variables in Generative Models.

Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Support and Invertibility in Domain-Invariant Representations.

Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

A probabilistic approach to discovering dynamic full-brain functional connectivity patterns.

Jeremy R. Manning, Xia Zhu, Theodore L. Willke, Rajesh Ranganath, Kimberly L. Stachenfeld, Uri Hasson, David M. Blei, Kenneth A. Norman

NeuroImage, 2018

Opportunities in Machine Learning for Healthcare.

Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath

CoRR, 2018

Multiple Causal Inference with Latent Confounding.

Rajesh Ranganath, Adler J. Perotte

CoRR, 2018

Max-margin learning with the Bayes factor.

Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Deep Survival Analysis: Nonparametrics and Missingness.

Xenia Miscouridou, Adler J. Perotte, Noemie Elhadad, Rajesh Ranganath

Proceedings of the Machine Learning for Healthcare Conference, 2018

Noisin: Unbiased Regularization for Recurrent Neural Networks.

Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei

Proceedings of the 35th International Conference on Machine Learning, 2018

Variational Sequential Monte Carlo.

Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Proximity Variational Inference.

Jaan Altosaar, Rajesh Ranganath, David M. Blei

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Automatic Differentiation Variational Inference.

Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei

J. Mach. Learn. Res., 2017

Deep and Hierarchical Implicit Models.

Dustin Tran, Rajesh Ranganath, David M. Blei

CoRR, 2017

Hierarchical Implicit Models and Likelihood-Free Variational Inference.

Dustin Tran, Rajesh Ranganath, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Inference via \chi Upper Bound Minimization.

Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Variational Gaussian Process.

Dustin Tran, Rajesh Ranganath, David M. Blei

Proceedings of the 4th International Conference on Learning Representations, 2016

The $χ$-Divergence for Approximate Inference.

Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei

CoRR, 2016

Operator Variational Inference.

Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei

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

Deep Survival Analysis.

Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei

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

Hierarchical Variational Models.

Rajesh Ranganath, Dustin Tran, David M. Blei

Proceedings of the 33nd International Conference on Machine Learning, 2016

Variational Tempering.

Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

Adler J. Perotte, Rajesh Ranganath, Jamie S. Hirsch, David M. Blei, Noémie Elhadad

J. Am. Medical Informatics Assoc., 2015

The Survival Filter: Joint Survival Analysis with a Latent Time Series.

Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Dynamic Poisson Factorization.

Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei

Proceedings of the 9th ACM Conference on Recommender Systems, 2015

The Population Posterior and Bayesian Modeling on Streams.

James McInerney, Rajesh Ranganath, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Automatic Variational Inference in Stan.

Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Deep Exponential Families.

Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Deterministic Annealing for Stochastic Variational Inference.

Farhan Abrol, Stephan Mandt, Rajesh Ranganath, David M. Blei

CoRR, 2014

Hierarchical topographic factor analysis.

Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, David M. Blei

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Black Box Variational Inference.

Rajesh Ranganath, Sean Gerrish, David M. Blei

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Bayesian Nonparametric Poisson Factorization for Recommendation Systems.

Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates.

Rajesh Ranganath, Dan Jurafsky, Daniel A. McFarland

Comput. Speech Lang., 2013

An Adaptive Learning Rate for Stochastic Variational Inference.

Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing

Proceedings of the 30th International Conference on Machine Learning, 2013

2011

Unsupervised learning of hierarchical representations with convolutional deep belief networks.

Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng

Commun. ACM, 2011

2009

Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation.

Daniel Jurafsky, Rajesh Ranganath, Daniel A. McFarland

Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, May 31, 2009

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.

Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates.

Rajesh Ranganath, Daniel Jurafsky, Daniel A. McFarland

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009