Rajesh Ranganath

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