It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates. - Rajesh Ranganath; Daniel Jurafsky; Daniel A. McFarland (EMNLP, 2009)

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. - Honglak Lee; Roger B. Grosse; Rajesh Ranganath; Andrew Y. Ng (ICML, 2009)

Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation. - Daniel Jurafsky; Rajesh Ranganath; Daniel A. McFarland (HLT-NAACL, 2009)

Unsupervised learning of hierarchical representations with convolutional deep belief networks. - Honglak Lee; Roger B. Grosse; Rajesh Ranganath; Andrew Y. Ng (Commun. ACM, 2011)

An Adaptive Learning Rate for Stochastic Variational Inference. - Rajesh Ranganath; Chong Wang 002; David M. Blei; Eric P. Xing (ICML (2), 2013)

Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates. - Rajesh Ranganath; Dan Jurafsky; Daniel A. McFarland (Comput. Speech Lang., 2013)

Bayesian Nonparametric Poisson Factorization for Recommendation Systems. - Prem Gopalan; Francisco J. R. Ruiz; Rajesh Ranganath; David M. Blei (AISTATS, 2014)

Black Box Variational Inference. - Rajesh Ranganath; Sean Gerrish; David M. Blei (AISTATS, 2014)

Hierarchical topographic factor analysis. - Jeremy R. Manning; Rajesh Ranganath; Waitsang Keung; Nicholas B. Turk-Browne; Jonathan D. Cohen 003; Kenneth A. Norman; David M. Blei (PRNI, 2014)

Deterministic Annealing for Stochastic Variational Inference. - Farhan Abrol; Stephan Mandt; Rajesh Ranganath; David M. Blei (CoRR, 2014)

Deep Exponential Families. - Rajesh Ranganath; Linpeng Tang; Laurent Charlin; David M. Blei (AISTATS, 2015)

Automatic Variational Inference in Stan. - Alp Kucukelbir; Rajesh Ranganath; Andrew Gelman; David M. Blei (NIPS, 2015)

The Population Posterior and Bayesian Modeling on Streams. - James McInerney; Rajesh Ranganath; David M. Blei (NIPS, 2015)

Dynamic Poisson Factorization. - Laurent Charlin; Rajesh Ranganath; James McInerney; David M. Blei (RecSys, 2015)

The Survival Filter: Joint Survival Analysis with a Latent Time Series. - Rajesh Ranganath; Adler J. Perotte; Noémie Elhadad; David M. Blei (UAI, 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)

Variational Tempering. - Stephan Mandt; James McInerney; Farhan Abrol; Rajesh Ranganath; David M. Blei (AISTATS, 2016)

Hierarchical Variational Models. - Rajesh Ranganath; Dustin Tran; David M. Blei (ICML, 2016)

Deep Survival Analysis. - Rajesh Ranganath; Adler J. Perotte; Noémie Elhadad; David M. Blei (MLHC, 2016)

Operator Variational Inference. - Rajesh Ranganath; Dustin Tran; Jaan Altosaar; David M. Blei (NIPS, 2016)

The $χ$-Divergence for Approximate Inference. - Adji B. Dieng; Dustin Tran; Rajesh Ranganath; John W. Paisley; David M. Blei (CoRR, 2016)

Variational Gaussian Process. - Dustin Tran; Rajesh Ranganath; David M. Blei (ICLR, 2016)

Variational Inference via chi Upper Bound Minimization. - Adji Bousso Dieng; Dustin Tran; Rajesh Ranganath; John W. Paisley; David M. Blei (NIPS, 2017)

Hierarchical Implicit Models and Likelihood-Free Variational Inference. - Dustin Tran; Rajesh Ranganath; David M. Blei (NIPS, 2017)

Deep and Hierarchical Implicit Models. - Dustin Tran; Rajesh Ranganath; David M. Blei (CoRR, 2017)

Automatic Differentiation Variational Inference. - Alp Kucukelbir; Dustin Tran; Rajesh Ranganath; Andrew Gelman; David M. Blei (J. Mach. Learn. Res., 2017)

Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications - Rajesh Ranganath (2017)

Proximity Variational Inference. - Jaan Altosaar; Rajesh Ranganath; David M. Blei (AISTATS, 2018)

Variational Sequential Monte Carlo. - Christian A. Naesseth; Scott W. Linderman; Rajesh Ranganath; David M. Blei (AISTATS, 2018)

Noisin: Unbiased Regularization for Recurrent Neural Networks. - Adji Bousso Dieng; Rajesh Ranganath; Jaan Altosaar; David M. Blei (ICML, 2018)

Deep Survival Analysis: Nonparametrics and Missingness. - Xenia Miscouridou; Adler J. Perotte; Noemie Elhadad; Rajesh Ranganath (MLHC, 2018)

Max-margin learning with the Bayes factor. - Rahul G. Krishnan; Arjun Khandelwal; Rajesh Ranganath; David A. Sontag (UAI, 2018)

Multiple Causal Inference with Latent Confounding. - Rajesh Ranganath; Adler J. Perotte (CoRR, 2018)

Opportunities in Machine Learning for Healthcare. - Marzyeh Ghassemi; Tristan Naumann; Peter Schulam; Andrew L. Beam; Rajesh Ranganath (CoRR, 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)

Support and Invertibility in Domain-Invariant Representations. - Fredrik D. Johansson; David A. Sontag; Rajesh Ranganath (AISTATS, 2019)

Revisiting Auxiliary Latent Variables in Generative Models. - Dieterich Lawson; George Tucker; Bo Dai 001; Rajesh Ranganath (DGS@ICLR, 2019)

Reproducibility in Machine Learning for Health. - Matthew B. A. McDermott; Shirly Wang; Nikki Marinsek; Rajesh Ranganath; Marzyeh Ghassemi; Luca Foschini 002 (RML@ICLR, 2019)

The Variational Predictive Natural Gradient. - Da Tang; Rajesh Ranganath (ICML, 2019)

Predicate Exchange: Inference with Declarative Knowledge. - Zenna Tavares; Javier Burroni; Edgar Minasyan; Armando Solar-Lezama; Rajesh Ranganath (ICML, 2019)

Energy-Inspired Models: Learning with Sampler-Induced Distributions. - Dieterich Lawson; George Tucker; Bo Dai 001; Rajesh Ranganath (NeurIPS, 2019)

Soft Constraints for Inference with Declarative Knowledge. - Zenna Tavares; Javier Burroni; Edgar Minaysan; Armando Solar-Lezama; Rajesh Ranganath (CoRR, 2019)

The Random Conditional Distribution for Higher-Order Probabilistic Inference. - Zenna Tavares; Xin Zhang 035; Edgar Minaysan; Javier Burroni; Rajesh Ranganath; Armando Solar-Lezama (CoRR, 2019)

Kernelized Complete Conditional Stein Discrepancy. - Raghav Singhal; Saad Lahlou; Rajesh Ranganath (CoRR, 2019)

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission. - Kexin Huang; Jaan Altosaar; Rajesh Ranganath (CoRR, 2019)

Adversarial Examples for Electrocardiograms. - Xintian Han; Yuxuan Hu; Luca Foschini 002; Larry Chinitz; Lior Jankelson; Rajesh Ranganath (CoRR, 2019)

Generalized Control Functions via Variational Decoupling. - Aahlad Manas Puli; Rajesh Ranganath (CoRR, 2019)

Population Predictive Checks. - Rajesh Ranganath; David M. Blei (CoRR, 2019)

Adversarially-Learned Balancing Weights for Causal Inference. - Amelia J. Averitt; Natnicha Vanitchanant; Rajesh Ranganath; Adler J. Perotte (AMIA, 2020)

Deep Survival Analysis: The Impact of Feature Missingness. - Shreyas Bhave; Xintian Han; Rajesh Ranganath; Adler J. Perotte (AMIA, 2020)

X-CAL: Explicit Calibration for Survival Analysis. - Mark Goldstein; Xintian Han; Aahlad Manas Puli; Adler J. Perotte; Rajesh Ranganath (NeurIPS, 2020)

Causal Estimation with Functional Confounders. - Aahlad Manas Puli; Adler J. Perotte; Rajesh Ranganath (NeurIPS, 2020)

General Control Functions for Causal Effect Estimation from IVs. - Aahlad Manas Puli; Rajesh Ranganath (NeurIPS, 2020)

Deep Direct Likelihood Knockoffs. - Mukund Sudarshan; Wesley Tansey; Rajesh Ranganath (NeurIPS, 2020)

The Counterfactual χ-GAN. - Amelia J. Averitt; Natnicha Vanitchanant; Rajesh Ranganath; Adler J. Perotte (CoRR, 2020)

Overfitting and Optimization in Offline Policy Learning. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (CoRR, 2020)

Probabilistic Machine Learning for Healthcare. - Irene Y. Chen; Shalmali Joshi; Marzyeh Ghassemi; Rajesh Ranganath (CoRR, 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)

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; et al. (npj Digit. Medicine, 2020)

Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. - Neil Jethani; Mukund Sudarshan; Yindalon Aphinyanaphongs; Rajesh Ranganath (AISTATS, 2021)

CONTRA: Contrarian statistics for controlled variable selection. - Mukund Sudarshan; Aahlad Manas Puli; Lakshmi Subramanian; Sriram Sankararaman; Rajesh Ranganath (AISTATS, 2021)

Offline Contextual Bandits with Overparameterized Models. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (ICML, 2021)

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. - Lily H. Zhang; Mark Goldstein; Rajesh Ranganath (ICML, 2021)

Offline RL Without Off-Policy Evaluation. - David Brandfonbrener; Will Whitney; Rajesh Ranganath; Joan Bruna (NeurIPS, 2021)

Inverse-Weighted Survival Games. - Xintian Han; Mark Goldstein; Aahlad Manas Puli; Thomas Wies; Adler J. Perotte; Rajesh Ranganath (NeurIPS, 2021)

Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations. - Aahlad Manas Puli; Lily H. Zhang; Eric K. Oermann; Rajesh Ranganath (CoRR, 2021)

Quantile Filtered Imitation Learning. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (CoRR, 2021)

Learning Invariant Representations with Missing Data. - Mark Goldstein; Jörn-Henrik Jacobsen; Olina Chau; Adriel Saporta; Aahlad Manas Puli; Rajesh Ranganath; Andrew C. Miller (CLeaR, 2022)

FastSHAP: Real-Time Shapley Value Estimation. - Neil Jethani; Mukund Sudarshan; Ian Connick Covert; Su-In Lee; Rajesh Ranganath (ICLR, 2022)

Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations. - Aahlad Manas Puli; Lily H. Zhang; Eric Karl Oermann; Rajesh Ranganath (ICLR, 2022)

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. - Lily H. Zhang; Veronica Tozzo; John M. Higgins; Rajesh Ranganath (ICML, 2022)

Survival Mixture Density Networks. - Xintian Han; Mark Goldstein; Rajesh Ranganath (MLHC, 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)

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)

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. - Aahlad Manas Puli; Nitish Joshi; He He 001; Rajesh Ranganath (CoRR, 2022)

Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection. - Lily H. Zhang; Rajesh Ranganath (AAAI, 2023)

Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. - Neil Jethani; Adriel Saporta; Rajesh Ranganath (AISTATS, 2023)

DIET: Conditional independence testing with marginal dependence measures of residual information. - Mukund Sudarshan; Aahlad Manas Puli; Wesley Tansey; Rajesh Ranganath (AISTATS, 2023)

Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. - Raghav Singhal; Mark Goldstein; Rajesh Ranganath (ICLR, 2023)

An Effective Meaningful Way to Evaluate Survival Models. - Shiang Qi; Neeraj Kumar 002; Mahtab Farrokh; Weijie Sun 004; Li-Hao Kuan; Rajesh Ranganath; Ricardo Henao; Russell Greiner (ICML, 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 (MLHC, 2023)

Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy. - Aahlad Manas Puli; Lily H. Zhang; Yoav Wald; Rajesh Ranganath (NeurIPS, 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)

Shortcut Learning Through the Lens of Early Training Dynamics. - Nihal Murali; Aahlad Manas Puli; Ke Yu; Rajesh Ranganath; Kayhan Batmanghelich (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; et al. (CoRR, 2023)

Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics. - Nihal Murali; Aahlad Manas Puli; Ke Yu; Rajesh Ranganath; Kayhan Batmanghelich (Trans. Mach. Learn. Res., 2023)

Stochastic Interpolants with Data-Dependent Couplings. - Michael S. Albergo; Mark Goldstein; Nicholas Matthew Boffi; Rajesh Ranganath; Eric Vanden-Eijnden (ICML, 2024)

What's the score? Automated Denoising Score Matching for Nonlinear Diffusions. - Raghav Singhal; Mark Goldstein; Rajesh Ranganath (ICML, 2024)

Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction. - Chen-Yu Yen; Raghav Singhal; Umang Sharma; Rajesh Ranganath; Sumit Chopra; Lerrel Pinto (ICML, 2024)

Preference Learning Algorithms Do Not Learn Preference Rankings. - Angelica Chen; Sadhika Malladi; Lily H. Zhang; Xinyi Chen 001; Qiuyi (Richard) Zhang; Rajesh Ranganath; Kyunghyun Cho (NeurIPS, 2024)

Explanations that reveal all through the definition of encoding. - Aahlad Manas Puli; Nhi Nguyen; Rajesh Ranganath (NeurIPS, 2024)

Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities. - Adriel Saporta; Aahlad Manas Puli; Mark Goldstein; Rajesh Ranganath (NeurIPS, 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 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (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 (Mach. Learn. Sci. Technol., 2024)

Quantifying impairment and disease severity using AI models trained on healthy subjects. - Boyang Yu 003; Aakash Kaku; Kangning Liu; Avinash Parnandi 001; Emily Fokas; Anita Venkatesan; Natasha Pandit; Rajesh Ranganath; Heidi M. Schambra; Carlos Fernandez-Granda (npj Digit. Medicine, 2024)

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. - Aahlad Manas Puli; Nitish Joshi; Yoav Wald; He He 001; Rajesh Ranganath (Trans. Mach. Learn. Res., 2024)

Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. - Lily H. Zhang; Rajesh Ranganath; Arya Tafvizi (Trans. Mach. Learn. Res., 2024)

From algorithms to action: improving patient care requires causality. - Wouter A. C. van Amsterdam; Pim A. de Jong; Joost J. C. Verhoeff; Tim Leiner; Rajesh Ranganath (BMC Medical Informatics Decis. Mak., 2024)

Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do. - Yoav Wald; Mark Goldstein; Yonathan Efroni; Wouter A. C. van Amsterdam; Rajesh Ranganath (ICLR, 2025)

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. - 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; et al. (J. Am. Medical Informatics Assoc., 2025)

When accurate prediction models yield harmful self-fulfilling prophecies. - Wouter A. C. van Amsterdam; Nan van Geloven; Jesse H. Krijthe; Rajesh Ranganath; Giovanni Cinà (Patterns, 2025)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Clara Meister; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)

A General Framework for Inference-time Scaling and Steering of Diffusion Models. - Raghav Singhal; Zachary Horvitz; Ryan Teehan; Mengye Ren; Zhou Yu; Kathleen McKeown; Rajesh Ranganath (CoRR, 2025)

Preference learning made easy: Everything should be understood through win rate. - Lily H. Zhang; Rajesh Ranganath (CoRR, 2025)

Black Box Causal Inference: Effect Estimation via Meta Prediction. - Lucius E. J. Bynum; Aahlad Manas Puli; Diego Herrero-Quevedo; Nhi Nguyen; Carlos Fernandez-Granda; Kyunghyun Cho; Rajesh Ranganath (CoRR, 2025)