← Authors

Rajesh Ranganath

55 papers · 2009–2025 · 10 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (10) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (8) 🐝 Cross-Pollinator (10) πŸƒ Academic Marathon (16) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (20) 🧭 Keyword Pioneer 🀝 Dynamic Duo (14) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (13) πŸ—ƒοΈ Keyword Collector (58) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (13) ⚑ Prolific Year (6) ❓ The Questioner (2) πŸ’Ž Century Club (55) πŸ“ˆ Trend Setter

Conferences

NIPS (16) ICML (14) AISTATS (11) ICLR (4) MLHC (4) EMNLP (2) AAAI (1) CLEAR (1) JMLR (1) NAACL (1)

Top co-authors

David Blei (14) Mark Goldstein (10) Aahlad Puli (9) Dustin Tran (5) Mukund Sudarshan (5) Lily Zhang (4) Raghav Singhal (4) Aahlad Manas Puli (4) Jaan Altosaar (3) Adler J. Perotte (3)

Keywords

variational inference (13) latent variable model (5) posterior approximation (5) survival analysis (5) bayesian inference (4) probabilistic programming (3) representation learning (3) domain adaptation (3) spurious correlation (3) posterior inference (3) deep generative model (3) learning rate (2) false discovery rate (2) causal inference (2) markov chain monte carlo (2) feature importance (2) feature attribution (2) latent dirichlet allocation (2) causal effect estimation (2) automatic differentiation (2)

Papers

Learning Is Not A Race: Improving Retrieval in Language Models via Equal Learning EMNLP 2025
Preference learning made easy: Everything should be understood through win rate ICML 2025
Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do ICLR 2025
A General Framework for Inference-time Scaling and Steering of Diffusion Models ICML 2025
Explanations that reveal all through the definition of encoding NIPS 2024
Preference Learning Algorithms Do Not Learn Preference Rankings NIPS 2024
Stochastic Interpolants with Data-Dependent Couplings ICML 2024
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction ICML 2024
What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions ICML 2024
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities NIPS 2024
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection AAAI 2023
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations MLHC 2023
An Effective Meaningful Way to Evaluate Survival Models ICML 2023
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions ICLR 2023
DIET: Conditional independence testing with marginal dependence measures of residual information AISTATS 2023
Don’t blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy NIPS 2023
Don’t be fooled: label leakage in explanation methods and the importance of their quantitative evaluation AISTATS 2023
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets ICML 2022
Learning Invariant Representations with Missing Data CLEAR 2022
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations ICLR 2022
FastSHAP: Real-Time Shapley Value Estimation ICLR 2022
Survival Mixture Density Networks MLHC 2022
Inverse-Weighted Survival Games NIPS 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. AISTATS 2021
Offline Contextual Bandits with Overparameterized Models ICML 2021
CONTRA: Contrarian statistics for controlled variable selection AISTATS 2021
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models ICML 2021
Offline RL Without Off-Policy Evaluation NIPS 2021
Deep Direct Likelihood Knockoffs NIPS 2020
Causal Estimation with Functional Confounders NIPS 2020
X-CAL: Explicit Calibration for Survival Analysis NIPS 2020
General Control Functions for Causal Effect Estimation from IVs NIPS 2020
Energy-Inspired Models: Learning with Sampler-Induced Distributions NIPS 2019
Predicate Exchange: Inference with Declarative Knowledge ICML 2019
Support and Invertibility in Domain-Invariant Representations AISTATS 2019
The Variational Predictive Natural Gradient ICML 2019
Variational Sequential Monte Carlo AISTATS 2018
Deep Survival Analysis: Nonparametrics and Missingness MLHC 2018
Noisin: Unbiased Regularization for Recurrent Neural Networks ICML 2018
Proximity Variational Inference AISTATS 2018
Hierarchical Implicit Models and Likelihood-Free Variational Inference NIPS 2017
Automatic Differentiation Variational Inference JMLR 2017
Variational Inference via $\chi$ Upper Bound Minimization NIPS 2017
Hierarchical Variational Models ICML 2016
Operator Variational Inference NIPS 2016
Deep Survival Analysis MLHC 2016
Variational Tempering AISTATS 2016
Automatic Variational Inference in Stan NIPS 2015
Deep Exponential Families AISTATS 2015
The Population Posterior and Bayesian Modeling on Streams NIPS 2015
Black Box Variational Inference AISTATS 2014
Bayesian Nonparametric Poisson Factorization for Recommendation Systems AISTATS 2014
An Adaptive Learning Rate for Stochastic Variational Inference ICML 2013
It’s Not You, it’s Me: Detecting Flirting and its Misperception in Speed-Dates EMNLP 2009
Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation NAACL 2009