Nalisnick, Eric

41 publications

TMLR 2026 Learning to Defer with an Uncertain Rejector via Conformal Prediction Yizirui Fang, Eric Nalisnick

ICLR 2025 Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence Dharmesh Tailor, Alvaro Correia, Eric Nalisnick, Christos Louizos

ICLR 2025 ELBOing Stein: Variational Bayes with Stein Mixture Inference Ola Rønning, Eric Nalisnick, Christophe Ley, Padhraic Smyth, Thomas Hamelryck

UAI 2025 Generative Uncertainty in Diffusion Models Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick, Stephan Mandt

ICLRW 2025 Generative Uncertainty in Diffusion Models Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick, Stephan Mandt

MLOSS 2025 Lightning UQ Box: Uncertainty Quantification for Neural Networks Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric Nalisnick

AISTATS 2025 Max-Rank: Efficient Multiple Testing for Conformal Prediction Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Christian A. Naesseth, Eric Nalisnick

NeurIPS 2025 Monitoring Risks in Test-Time Adaptation Mona Schirmer, Metod Jazbec, Christian A. Naesseth, Eric Nalisnick

UAI 2025 On Continuous Monitoring of Risk Violations Under Unknown Shift Alexander Timans, Rajeev Verma, Eric Nalisnick, Christian A. Naesseth

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

TMLR 2025 Scalable Generative Modeling of Weighted Graphs Richard Williams, Eric Nalisnick, Andrew Holbrook

TMLR 2025 Temporal Test-Time Adaptation with State-Space Models Mona Schirmer, Dan Zhang, Eric Nalisnick

ICLRW 2025 [TINY] Vision Language Models Can Implicitly Quantify Aleatoric Uncertainty Xi Wang, Eric Nalisnick

NeurIPS 2024 A Generative Model of Symmetry Transformations James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric Nalisnick, José Miguel Hernández-Lobato

ECCV 2024 Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric Nalisnick

UAI 2024 Early-Exit Neural Networks with Nested Prediction Sets Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric Nalisnick

NeurIPS 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

ICMLW 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

ICMLW 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

AISTATS 2024 Learning to Defer to a Population: A Meta-Learning Approach Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric Nalisnick

NeurIPSW 2024 Learning to Defer with an Uncertain Rejector via Conformal Prediction Yizirui Fang, Eric Nalisnick

ICMLW 2024 On the Calibration of Conditional-Value-at-Risk Rajeev Verma, Volker Fischer, Eric Nalisnick

TMLR 2024 Practical Synthesis of Mixed-Tailed Data with Normalizing Flows Saba Amiri, Eric Nalisnick, Adam Belloum, Sander Klous, Leon Gommans

ICMLW 2024 Test-Time Adaptation with State-Space Models Mona Schirmer, Dan Zhang, Eric Nalisnick

NeurIPSW 2023 Beyond Top-Class Agreement: Using Divergences to Forecast Performance Under Distribution Shift Mona Schirmer, Dan Zhang, Eric Nalisnick

AISTATS 2023 Do Bayesian Neural Networks Need to Be Fully Stochastic? Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, Tom Rainforth

UAI 2023 Exploiting Inferential Structure in Neural Processes Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric Nalisnick

AISTATS 2023 Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles Rajeev Verma, Daniel Barrejon, Eric Nalisnick

ICLR 2023 Sampling-Based Inference for Large Linear Models, with Application to Linearised Laplace Javier Antoran, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato

NeurIPS 2023 Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity Metod Jazbec, James Allingham, Dan Zhang, Eric Nalisnick

ICML 2022 Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antoran, David Janz, James U Allingham, Erik Daxberger, Riccardo Rb Barbano, Eric Nalisnick, Jose Miguel Hernandez-Lobato

ICML 2022 Calibrated Learning to Defer with One-vs-All Classifiers Rajeev Verma, Eric Nalisnick

NeurIPSW 2022 Learning Generative Models with Invariance to Symmetries James Urquhart Allingham, Javier Antoran, Shreyas Padhy, Eric Nalisnick, José Miguel Hernández-Lobato

AISTATS 2021 Predictive Complexity Priors Eric Nalisnick, Jonathan Gordon, Jose Miguel Hernandez-Lobato

ICML 2021 Bayesian Deep Learning via Subnetwork Inference Erik Daxberger, Eric Nalisnick, James U Allingham, Javier Antoran, Jose Miguel Hernandez-Lobato

JMLR 2021 Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

NeurIPS 2019 Bayesian Batch Active Learning as Sparse Subset Approximation Robert Pinsler, Jonathan Gordon, Eric Nalisnick, José Miguel Hernández-Lobato

ICLR 2019 Do Deep Generative Models Know What They Don't Know? Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

ICML 2019 Dropout as a Structured Shrinkage Prior Eric Nalisnick, Jose Miguel Hernandez-Lobato, Padhraic Smyth

ICML 2019 Hybrid Models with Deep and Invertible Features Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

MLHC 2018 Bayesian Trees for Automated Cytometry Data Analysis Disi Ji, Eric Nalisnick, Yu Qian, Richard H. Scheuermann, Padhraic Smyth