Rajesh Ranganath

163 papers A* 32A 14Misc 2Journal 90Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	To Use or not to Use Muon: How Simplicity Bias in Optimizers Matters. CoRR Sara Dragutinovic, Rajesh Ranganath
2025	A*	`conf`	A General Framework for Inference-time Scaling and Steering of Diffusion Models. ICML Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, Rajesh Ranganath
2025	J	`jnl`	A General Framework for Inference-time Scaling and Steering of Diffusion Models. CoRR Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, Rajesh Ranganath
2025	J	`jnl`	AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. J. Am. Medical Informatics Assoc. Shalmali Joshi, Iñigo Urteaga, Wouter A. C. van Amsterdam, George Hripcsak, Pierre A. Elias, Benjamin R. C. Amor, Noémie Elhadad, James C. Fackler, Mark P. Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary C. Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E. Vogt, Rajesh Ranganath
2025	J	`jnl`	Attention and Compression is all you need for Controllably Efficient Language Models. CoRR Jatin Prakash, Aahlad Manas Puli, Rajesh Ranganath
2025	J	`jnl`	Black Box Causal Inference: Effect Estimation via Meta Prediction. CoRR Lucius E. J. Bynum, Aahlad Manas Puli, Diego Herrero-Quevedo, Nhi Nguyen, Carlos Fernandez-Granda, Kyunghyun Cho, Rajesh Ranganath
2025	J	`jnl`	KL-Regularized Reinforcement Learning is Designed to Mode Collapse. CoRR Anthony GX-Chen, Jatin Prakash, Jeff Guo, Rob Fergus, Rajesh Ranganath
2025	—	`conf`	Learning Is Not A Race: Improving Retrieval in Language Models via Equal Learning. EMNLP (Findings) Wanqian Yang, Aahlad Manas Puli, Rajesh Ranganath
2025	J	`jnl`	No Compute Left Behind: Rethinking Reasoning and Sampling with Masked Diffusion Models. CoRR Zachary Horvitz, Raghav Singhal, Hao Zou, Carles Domingo-Enrich, Zhou Yu, Rajesh Ranganath, Kathleen McKeown
2025	J	`jnl`	On the Challenges and Opportunities in Generative AI. Trans. Mach. Learn. Res. Laura Manduchi, Clara Meister, 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
2025	A*	`conf`	Preference learning made easy: Everything should be understood through win rate. ICML Lily H. Zhang, Rajesh Ranganath
2025	J	`jnl`	Preference learning made easy: Everything should be understood through win rate. CoRR Lily H. Zhang, Rajesh Ranganath
2025	J	`jnl`	Three Forms of Stochastic Injection for Improved Distribution-to-Distribution Generative Modeling. CoRR Shiye Su, Yuhui Zhang, Linqi Zhou, Rajesh Ranganath, Serena Yeung-Levy
2025	A*	`conf`	Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do. ICLR Yoav Wald, Mark Goldstein, Yonathan Efroni, Wouter A. C. van Amsterdam, Rajesh Ranganath
2025	J	`jnl`	Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do. CoRR Yoav Wald, Mark Goldstein, Yonathan Efroni, Wouter A. C. van Amsterdam, Rajesh Ranganath
2025	J	`jnl`	When accurate prediction models yield harmful self-fulfilling prophecies. Patterns Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, Giovanni Cinà
2024	A*	`conf`	Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction. ICML Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit Chopra, Lerrel Pinto
2024	J	`jnl`	Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction. CoRR Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit Chopra, Lerrel Pinto
2024	A*	`conf`	Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities. NeurIPS Adriel Saporta, Aahlad Manas Puli, Mark Goldstein, Rajesh Ranganath
2024	J	`jnl`	Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities. CoRR Adriel Saporta, Aahlad Manas Puli, Mark Goldstein, Rajesh Ranganath
2024	A*	`conf`	Explanations that reveal all through the definition of encoding. NeurIPS Aahlad Manas Puli, Nhi Nguyen, Rajesh Ranganath
2024	J	`jnl`	Explanations that reveal all through the definition of encoding. CoRR Aahlad Manas Puli, Nhi Nguyen, Rajesh Ranganath
2024	J	`jnl`	From algorithms to action: improving patient care requires causality. BMC Medical Informatics Decis. Mak. Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath
2024	—	`ed.`	Machine Learning for Healthcare Conference, 16-17 August 2024, Toronto, Canada. MLHC Kaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary C. Lipton, Rajesh Ranganath, Iñigo Urteaga
2024	J	`jnl`	Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. Trans. Mach. Learn. Res. Aahlad Manas Puli, Nitish Joshi, Yoav Wald, He He, Rajesh Ranganath
2024	J	`jnl`	On the Challenges and Opportunities in Generative AI. CoRR 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
2024	A*	`conf`	Preference Learning Algorithms Do Not Learn Preference Rankings. NeurIPS Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi (Richard) Zhang, Rajesh Ranganath, Kyunghyun Cho
2024	J	`jnl`	Preference Learning Algorithms Do Not Learn Preference Rankings. CoRR Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, Kyunghyun Cho
2024	J	`jnl`	Quantifying impairment and disease severity using AI models trained on healthy subjects. npj Digit. Medicine Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi M. Schambra, Carlos Fernandez-Granda
2024	J	`jnl`	Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning. CoRR Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
2024	J	`jnl`	Robust anomaly detection for particle physics using multi-background representation learning. Mach. Learn. Sci. Technol. Abhijith Gandrakota, Lily H. Zhang, Aahlad Manas Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
2024	A*	`conf`	Stochastic Interpolants with Data-Dependent Couplings. ICML Michael S. Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
2024	J	`jnl`	Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. CoRR Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi
2024	J	`jnl`	Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. Trans. Mach. Learn. Res. Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi
2024	A*	`conf`	What's the score? Automated Denoising Score Matching for Nonlinear Diffusions. ICML Raghav Singhal, Mark Goldstein, Rajesh Ranganath
2024	J	`jnl`	What's the score? Automated Denoising Score Matching for Nonlinear Diffusions. CoRR Raghav Singhal, Mark Goldstein, Rajesh Ranganath
2023	J	`jnl`	A dynamic risk score for early prediction of cardiogenic shock using machine learning. CoRR 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
2023	A*	`conf`	An Effective Meaningful Way to Evaluate Survival Models. ICML Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner
2023	J	`jnl`	An Effective Meaningful Way to Evaluate Survival Models. CoRR Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner
2023	J	`jnl`	Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics. Trans. Mach. Learn. Res. Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich
2023	A	`conf`	DIET: Conditional independence testing with marginal dependence measures of residual information. AISTATS Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath
2023	A	`conf`	Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. AISTATS Neil Jethani, Adriel Saporta, Rajesh Ranganath
2023	J	`jnl`	Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. CoRR Neil Jethani, Adriel Saporta, Rajesh Ranganath
2023	A*	`conf`	Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy. NeurIPS Aahlad Manas Puli, Lily H. Zhang, Yoav Wald, Rajesh Ranganath
2023	J	`jnl`	Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy. CoRR Aahlad Manas Puli, Lily H. Zhang, Yoav Wald, Rajesh Ranganath
2023	—	`ed.`	Machine Learning for Healthcare Conference, MLHC 2023, 11-12 August 2023, New York, USA. MLHC Kaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary C. Lipton, Rajesh Ranganath, Iñigo Urteaga, Serene Yeung
2023	J	`jnl`	On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease. CoRR Raghav Singhal, Mukund Sudarshan, Anish Mahishi, Sri Kaushik, Luke Ginocchio, Angela Tong, Hersh Chandarana, Daniel K. Sodickson, Rajesh Ranganath, Sumit Chopra
2023	J	`jnl`	Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects. CoRR Boyang Yu, Aakash Kaku, Kangning Liu, Avinash Parnandi, Emily Fokas, Anita Venkatesan, Natasha Pandit, Rajesh Ranganath, Heidi M. Schambra, Carlos Fernandez-Granda
2023	A*	`conf`	Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection. AAAI Lily H. Zhang, Rajesh Ranganath
2023	J	`jnl`	Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection. CoRR Lily H. Zhang, Rajesh Ranganath
2023	J	`jnl`	Shortcut Learning Through the Lens of Early Training Dynamics. CoRR Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich
2023	J	`jnl`	Stochastic interpolants with data-dependent couplings. CoRR Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
2023	—	`conf`	When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. MLHC Rhys Compton, Lily H. Zhang, Aahlad Manas Puli, Rajesh Ranganath
2023	J	`jnl`	When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. CoRR Rhys Compton, Lily H. Zhang, Aahlad Manas Puli, Rajesh Ranganath
2023	J	`jnl`	When accurate prediction models yield harmful self-fulfilling prophecies. CoRR Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, Giovanni Ciná
2023	A*	`conf`	Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. ICLR Raghav Singhal, Mark Goldstein, Rajesh Ranganath
2023	J	`jnl`	Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. CoRR Raghav Singhal, Mark Goldstein, Rajesh Ranganath
2022	J	`jnl`	DIET: Conditional independence testing with marginal dependence measures of residual information. CoRR Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath
2022	J	`jnl`	Decision making in cancer: Causal questions require causal answers. CoRR Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath
2022	A*	`conf`	FastSHAP: Real-Time Shapley Value Estimation. ICLR Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath
2022	—	`conf`	Learning Invariant Representations with Missing Data. CLeaR Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller
2022	J	`jnl`	New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography. CoRR Neil Jethani, Aahlad Manas Puli, Hao Zhang, Leonid Garber, Lior Jankelson, Yindalon Aphinyanaphongs, Rajesh Ranganath
2022	J	`jnl`	Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. CoRR Aahlad Manas Puli, Nitish Joshi, He He, Rajesh Ranganath
2022	A*	`conf`	Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations. ICLR Aahlad Manas Puli, Lily H. Zhang, Eric Karl Oermann, Rajesh Ranganath
2022	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2022, 5-6 August 2022, Durham, NC, USA. MLHC Zachary C. Lipton, Rajesh Ranganath, Mark P. Sendak, Michael W. Sjoding, Serena Yeung
2022	A*	`conf`	Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. ICML Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath
2022	J	`jnl`	Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. CoRR Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath
2022	—	`conf`	Survival Mixture Density Networks. MLHC Xintian Han, Mark Goldstein, Rajesh Ranganath
2022	J	`jnl`	Survival Mixture Density Networks. CoRR Xintian Han, Mark Goldstein, Rajesh Ranganath
2021	A	`conf`	CONTRA: Contrarian statistics for controlled variable selection. AISTATS Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath
2021	J	`jnl`	Causal Estimation with Functional Confounders. CoRR Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath
2021	J	`jnl`	FastSHAP: Real-Time Shapley Value Estimation. CoRR Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath
2021	A	`conf`	Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. AISTATS Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath
2021	J	`jnl`	Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. CoRR Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath
2021	A*	`conf`	Inverse-Weighted Survival Games. NeurIPS Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath
2021	J	`jnl`	Inverse-Weighted Survival Games. CoRR Xintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath
2021	J	`jnl`	Learning Invariant Representations with Missing Data. CoRR Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller
2021	A*	`conf`	Offline Contextual Bandits with Overparameterized Models. ICML David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna
2021	A*	`conf`	Offline RL Without Off-Policy Evaluation. NeurIPS David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna
2021	J	`jnl`	Offline RL Without Off-Policy Evaluation. CoRR David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna
2021	J	`jnl`	Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations. CoRR Aahlad Manas Puli, Lily H. Zhang, Eric K. Oermann, Rajesh Ranganath
2021	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2021, 6-7 August 2021, Virtual Event. MLHC Ken Jung, Serena Yeung, Mark P. Sendak, Michael W. Sjoding, Rajesh Ranganath
2021	J	`jnl`	Quantile Filtered Imitation Learning. CoRR David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna
2021	A*	`conf`	Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. ICML Lily H. Zhang, Mark Goldstein, Rajesh Ranganath
2021	J	`jnl`	Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. CoRR Lily H. Zhang, Mark Goldstein, Rajesh Ranganath
2021	J	`jnl`	X-CAL: Explicit Calibration for Survival Analysis. CoRR Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath
2020	J	`jnl`	A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients. npj Digit. Medicine 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
2020	Misc	`conf`	Adversarially-Learned Balancing Weights for Causal Inference. AMIA Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte
2020	A*	`conf`	Causal Estimation with Functional Confounders. NeurIPS Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath
2020	A*	`conf`	Deep Direct Likelihood Knockoffs. NeurIPS Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
2020	J	`jnl`	Deep Direct Likelihood Knockoffs. CoRR Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
2020	Misc	`conf`	Deep Survival Analysis: The Impact of Feature Missingness. AMIA Shreyas Bhave, Xintian Han, Rajesh Ranganath, Adler J. Perotte
2020	A*	`conf`	General Control Functions for Causal Effect Estimation from IVs. NeurIPS Aahlad Manas Puli, Rajesh Ranganath
2020	J	`jnl`	Overfitting and Optimization in Offline Policy Learning. CoRR David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna
2020	J	`jnl`	Probabilistic Machine Learning for Healthcare. CoRR Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath
2020	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2020, 7-8 August 2020, Virtual Event, Durham, NC, USA. MLHC Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens
2020	J	`jnl`	The Counterfactual χ-GAN. CoRR Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte
2020	J	`jnl`	The Counterfactual χ-GAN: Finding comparable cohorts in observational health data. J. Biomed. Informatics Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte
2020	A*	`conf`	X-CAL: Explicit Calibration for Survival Analysis. NeurIPS Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath
2019	J	`jnl`	Adversarial Examples for Electrocardiograms. CoRR Xintian Han, Yuxuan Hu, Luca Foschini, Larry Chinitz, Lior Jankelson, Rajesh Ranganath
2019	J	`jnl`	ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission. CoRR Kexin Huang, Jaan Altosaar, Rajesh Ranganath
2019	A*	`conf`	Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2019	J	`jnl`	Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2019	J	`jnl`	Generalized Control Functions via Variational Decoupling. CoRR Aahlad Manas Puli, Rajesh Ranganath
2019	J	`jnl`	Kernelized Complete Conditional Stein Discrepancy. CoRR Raghav Singhal, Saad Lahlou, Rajesh Ranganath
2019	J	`jnl`	Population Predictive Checks. CoRR Rajesh Ranganath, David M. Blei
2019	A*	`conf`	Predicate Exchange: Inference with Declarative Knowledge. ICML Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath
2019	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA. MLHC Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens
2019	—	`conf`	Reproducibility in Machine Learning for Health. RML@ICLR Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini
2019	J	`jnl`	Reproducibility in Machine Learning for Health. CoRR Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini
2019	—	`conf`	Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2019	J	`jnl`	Soft Constraints for Inference with Declarative Knowledge. CoRR Zenna Tavares, Javier Burroni, Edgar Minaysan, Armando Solar-Lezama, Rajesh Ranganath
2019	A	`conf`	Support and Invertibility in Domain-Invariant Representations. AISTATS Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath
2019	J	`jnl`	Support and Invertibility in Domain-Invariant Representations. CoRR Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath
2019	J	`jnl`	The Random Conditional Distribution for Higher-Order Probabilistic Inference. CoRR Zenna Tavares, Xin Zhang, Edgar Minaysan, Javier Burroni, Rajesh Ranganath, Armando Solar-Lezama
2019	A*	`conf`	The Variational Predictive Natural Gradient. ICML Da Tang, Rajesh Ranganath
2019	J	`jnl`	The Variational Predictive Natural Gradient. CoRR Da Tang, Rajesh Ranganath
2018	J	`jnl`	A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage Jeremy R. Manning, Xia Zhu, Theodore L. Willke, Rajesh Ranganath, Kimberly L. Stachenfeld, Uri Hasson, David M. Blei, Kenneth A. Norman
2018	—	`conf`	Deep Survival Analysis: Nonparametrics and Missingness. MLHC Xenia Miscouridou, Adler J. Perotte, Noemie Elhadad, Rajesh Ranganath
2018	A	`conf`	Max-margin learning with the Bayes factor. UAI Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag
2018	J	`jnl`	Multiple Causal Inference with Latent Confounding. CoRR Rajesh Ranganath, Adler J. Perotte
2018	A*	`conf`	Noisin: Unbiased Regularization for Recurrent Neural Networks. ICML Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
2018	J	`jnl`	Noisin: Unbiased Regularization for Recurrent Neural Networks. CoRR Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
2018	J	`jnl`	Opportunities in Machine Learning for Healthcare. CoRR Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath
2018	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2018, 17-18 August 2018, Palo Alto, California MLHC Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens
2018	A	`conf`	Proximity Variational Inference. AISTATS Jaan Altosaar, Rajesh Ranganath, David M. Blei
2018	A	`conf`	Variational Sequential Monte Carlo. AISTATS Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
2017	J	`jnl`	Automatic Differentiation Variational Inference. J. Mach. Learn. Res. Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
2017	—	`—`	Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications Rajesh Ranganath
2017	J	`jnl`	Deep and Hierarchical Implicit Models. CoRR Dustin Tran, Rajesh Ranganath, David M. Blei
2017	—	`conf`	Hierarchical Implicit Models and Likelihood-Free Variational Inference. NIPS Dustin Tran, Rajesh Ranganath, David M. Blei
2017	—	`ed.`	Proceedings of the Machine Learning for Healthcare Conference, MLHC 2017, Boston, Massachusetts, USA, 18-19 August 2017 MLHC Finale Doshi-Velez, Jim Fackler, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens
2017	J	`jnl`	Proximity Variational Inference. CoRR Jaan Altosaar, Rajesh Ranganath, David M. Blei
2017	—	`conf`	Variational Inference via \chi Upper Bound Minimization. NIPS Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
2016	J	`jnl`	Automatic Differentiation Variational Inference. CoRR Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
2016	—	`conf`	Deep Survival Analysis. MLHC Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei
2016	J	`jnl`	Deep Survival Analysis. CoRR Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei
2016	A*	`conf`	Hierarchical Variational Models. ICML Rajesh Ranganath, Dustin Tran, David M. Blei
2016	—	`conf`	Operator Variational Inference. NIPS Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei
2016	J	`jnl`	Operator Variational Inference. CoRR Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei
2016	J	`jnl`	The $χ$-Divergence for Approximate Inference. CoRR Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
2016	A*	`conf`	Variational Gaussian Process. ICLR Dustin Tran, Rajesh Ranganath, David M. Blei
2016	A	`conf`	Variational Tempering. AISTATS Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei
2015	—	`conf`	Automatic Variational Inference in Stan. NIPS Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei
2015	A	`conf`	Deep Exponential Families. AISTATS Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
2015	A	`conf`	Dynamic Poisson Factorization. RecSys Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
2015	J	`jnl`	Dynamic Poisson Factorization. CoRR Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
2015	J	`jnl`	Hierarchical Variational Models. CoRR Rajesh Ranganath, Dustin Tran, David M. Blei
2015	J	`jnl`	Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J. Am. Medical Informatics Assoc. Adler J. Perotte, Rajesh Ranganath, Jamie S. Hirsch, David M. Blei, Noémie Elhadad
2015	—	`conf`	The Population Posterior and Bayesian Modeling on Streams. NIPS James McInerney, Rajesh Ranganath, David M. Blei
2015	A	`conf`	The Survival Filter: Joint Survival Analysis with a Latent Time Series. UAI Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei
2014	A	`conf`	Bayesian Nonparametric Poisson Factorization for Recommendation Systems. AISTATS Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei
2014	A	`conf`	Black Box Variational Inference. AISTATS Rajesh Ranganath, Sean Gerrish, David M. Blei
2014	J	`jnl`	Black Box Variational Inference. CoRR Rajesh Ranganath, Sean Gerrish, David M. Blei
2014	J	`jnl`	Deep Exponential Families. CoRR Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
2014	J	`jnl`	Deterministic Annealing for Stochastic Variational Inference. CoRR Farhan Abrol, Stephan Mandt, Rajesh Ranganath, David M. Blei
2014	—	`conf`	Hierarchical topographic factor analysis. PRNI Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, David M. Blei
2013	—	`conf`	An Adaptive Learning Rate for Stochastic Variational Inference. ICML (2) Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing
2013	J	`jnl`	Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates. Comput. Speech Lang. Rajesh Ranganath, Dan Jurafsky, Daniel A. McFarland
2011	J	`jnl`	Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng
2009	A*	`conf`	Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. ICML Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng
2009	—	`conf`	Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation. HLT-NAACL Daniel Jurafsky, Rajesh Ranganath, Daniel A. McFarland
2009	A*	`conf`	It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates. EMNLP Rajesh Ranganath, Daniel Jurafsky, Daniel A. McFarland