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Rajesh Ranganath

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

This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 11 separate collaboration groups - the largest holds only 49% of them. Pick the group below that matches the researcher you mean. How a shared name is split.

Achievements are not shown for a combined profile - each one describes a single researcher's record, which this is not.

Which Rajesh Ranganath?

5 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.

with Mark Goldstein, Aahlad Puli, Mukund Sudarshan 27 papers · 2020–2025 · NeurIPS, ICML, ICLR Hide ▲
A General Framework for Inference-time Scaling and Steering of Diffusion Models ICML 2025 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 Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction ICML 2024 Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities NeurIPS 2024 Explanations that reveal all through the definition of encoding NeurIPS 2024 Stochastic Interpolants with Data-Dependent Couplings ICML 2024 What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions ICML 2024 DIET: Conditional independence testing with marginal dependence measures of residual information AISTATS 2023 Don’t be fooled: label leakage in explanation methods and the importance of their quantitative evaluation AISTATS 2023 Don’t blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy NeurIPS 2023 When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations MLHC 2023 Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions ICLR 2023 FastSHAP: Real-Time Shapley Value Estimation ICLR 2022 Learning Invariant Representations with Missing Data CLeaR 2022 Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations ICLR 2022 Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets ICML 2022 Survival Mixture Density Networks MLHC 2022 CONTRA: Contrarian statistics for controlled variable selection AISTATS 2021 Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. AISTATS 2021 Inverse-Weighted Survival Games NeurIPS 2021 Understanding Failures in Out-of-Distribution Detection with Deep Generative Models ICML 2021 Causal Estimation with Functional Confounders NeurIPS 2020 Deep Direct Likelihood Knockoffs NeurIPS 2020 General Control Functions for Causal Effect Estimation from IVs NeurIPS 2020 X-CAL: Explicit Calibration for Survival Analysis NeurIPS 2020
6 papers whose co-authors appear nowhere else under this name
An Effective Meaningful Way to Evaluate Survival Models ICML 2023 Energy-Inspired Models: Learning with Sampler-Induced Distributions NeurIPS 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 An Adaptive Learning Rate for Stochastic Variational Inference ICML 2013

Venues · all papers under this name

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