ML Anthology

Ranganath, Rajesh

66 publications

ICML 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
TMLR 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, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric 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
ICML 2025 Preference Learning Made Easy: Everything Should Be Understood Through Win Rate Lily H Zhang, Rajesh Ranganath
NeurIPS 2025 Test Time Scaling for Neural Processes Hyungi Lee, Moonseok Choi, Hyunsu Kim, Kyunghyun Cho, Rajesh Ranganath, Juho Lee
ICLR 2025 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
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
NeurIPS 2024 Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities Adriel Saporta, Aahlad Puli, Mark Goldstein, Rajesh Ranganath
NeurIPS 2024 Explanations That Reveal All Through the Definition of Encoding Aahlad Puli, Nhi Nguyen, Rajesh Ranganath
TMLR 2024 Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation Aahlad Manas Puli, Nitish Joshi, Yoav Wald, He He, Rajesh Ranganath
NeurIPS 2024 Preference Learning Algorithms Do Not Learn Preference Rankings Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, Kyunghyun Cho
ICMLW 2024 Preference Learning Algorithms Do Not Learn Preference Rankings Angelica Chen, Sadhika Malladi, Lily H Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, Kyunghyun Cho
ICMLW 2024 Preference Learning Algorithms Do Not Learn Preference Rankings Angelica Chen, Sadhika Malladi, Lily H Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, Kyunghyun Cho
ICML 2024 Stochastic Interpolants with Data-Dependent Couplings Michael Samuel Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
TMLR 2024 Towards Minimal Targeted Updates of Language Models with Targeted Negative Training Lily H Zhang, Rajesh Ranganath, Arya Tafvizi
ICML 2024 What’s the Score? Automated Denoising Score Matching for Nonlinear Diffusions Raghav Singhal, Mark Goldstein, Rajesh Ranganath
ICML 2023 An Effective Meaningful Way to Evaluate Survival Models Shi-Ang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner
TMLR 2023 Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich
AISTATS 2023 DIET: Conditional Independence Testing with Marginal Dependence Measures of Residual Information Mukund Sudarshan, Aahlad Puli, Wesley Tansey, Rajesh Ranganath
AISTATS 2023 Don’t Be Fooled: Label Leakage in Explanation Methods and the Importance of Their Quantitative Evaluation Neil Jethani, Adriel Saporta, Rajesh Ranganath
NeurIPS 2023 Don’t Blame Dataset Shift! Shortcut Learning Due to Gradients and Cross Entropy Aahlad Manas Puli, Lily Zhang, Yoav Wald, Rajesh Ranganath
AAAI 2023 Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection Lily H. Zhang, Rajesh Ranganath
MLHC 2023 When More Is Less: Incorporating Additional Datasets Can Hurt Performance by Introducing Spurious Correlations Rhys Compton, Lily Zhang, Aahlad Puli, Rajesh Ranganath
ICLR 2023 Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions Raghav Singhal, Mark Goldstein, Rajesh Ranganath
ICLR 2022 FastSHAP: Real-Time Shapley Value Estimation Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath
CLeaR 2022 Learning Invariant Representations with Missing Data Mark Goldstein, Joern-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew Miller
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
ICML 2022 Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets Lily Zhang, Veronica Tozzo, John Higgins, Rajesh Ranganath
MLHC 2022 Survival Mixture Density Networks Xintian Han, Mark Goldstein, Rajesh Ranganath
AISTATS 2021 CONTRA: Contrarian Statistics for Controlled Variable Selection Mukund Sudarshan, Aahlad Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath
AISTATS 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
NeurIPS 2021 Inverse-Weighted Survival Games Xintian Han, Mark Goldstein, Aahlad Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath
NeurIPSW 2021 Learning Invariant Representations with Missing Data Mark Goldstein, Joern-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew Miller
ICML 2021 Offline Contextual Bandits with Overparameterized Models David Brandfonbrener, William Whitney, Rajesh Ranganath, Joan Bruna
NeurIPS 2021 Offline RL Without Off-Policy Evaluation David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna
ICML 2021 Understanding Failures in Out-of-Distribution Detection with Deep Generative Models Lily Zhang, Mark Goldstein, Rajesh Ranganath
NeurIPS 2020 Causal Estimation with Functional Confounders Aahlad Puli, Adler J. Perotte, Rajesh Ranganath
NeurIPS 2020 Deep Direct Likelihood Knockoffs Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
NeurIPS 2020 General Control Functions for Causal Effect Estimation from IVs Aahlad Puli, Rajesh Ranganath
ICMLW 2020 Information Theoretic Approaches for Testing Missingness in Predictive Models Shreyas A Bhave, Rajesh Ranganath, Adler Perotte
NeurIPS 2020 X-CAL: Explicit Calibration for Survival Analysis Mark Goldstein, Xintian Han, Aahlad Puli, Adler J. Perotte, Rajesh Ranganath
NeurIPS 2019 Energy-Inspired Models: Learning with Sampler-Induced Distributions John Lawson, George Tucker, Bo Dai, Rajesh Ranganath
ICML 2019 Predicate Exchange: Inference with Declarative Knowledge Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath
ICLRW 2019 Revisiting Auxiliary Latent Variables in Generative Models Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
AISTATS 2019 Support and Invertibility in Domain-Invariant Representations Fredrik D. Johansson, David Sontag, Rajesh Ranganath
ICML 2019 The Variational Predictive Natural Gradient Da Tang, Rajesh Ranganath
MLHC 2018 Deep Survival Analysis: Nonparametrics and Missingness Xenia Miscouridou, Adler Perotte, Noemie Elhadad, Rajesh Ranganath
UAI 2018 Max-Margin Learning with the Bayes Factor Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag
ICML 2018 Noisin: Unbiased Regularization for Recurrent Neural Networks Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David Blei
AISTATS 2018 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
JMLR 2017 Automatic Differentiation Variational Inference Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
NeurIPS 2017 Hierarchical Implicit Models and Likelihood-Free Variational Inference Dustin Tran, Rajesh Ranganath, David Blei
NeurIPS 2017 Variational Inference via $\chi$ Upper Bound Minimization Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei
MLHC 2016 Deep Survival Analysis Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei
ICML 2016 Hierarchical Variational Models Rajesh Ranganath, Dustin Tran, David Blei
NeurIPS 2016 Operator Variational Inference Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David Blei
ICLR 2016 Variational Gaussian Process Dustin Tran, Rajesh Ranganath, David M. Blei
AISTATS 2016 Variational Tempering Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei
NeurIPS 2015 Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei
AISTATS 2015 Deep Exponential Families Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
NeurIPS 2015 The Population Posterior and Bayesian Modeling on Streams James McInerney, Rajesh Ranganath, David Blei
UAI 2015 The Survival Filter: Joint Survival Analysis with a Latent Time Series Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei
AISTATS 2014 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
ICML 2013 An Adaptive Learning Rate for Stochastic Variational Inference Rajesh Ranganath, Chong Wang, Blei David, Eric Xing
ICML 2009 Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng