Eric T. Nalisnick

92 papers A* 13A 12B 1Journal 53Unranked 12
YearRankTypeTitle / Venue / Authors
2026 J jnl
Are vision language models robust to classic uncertainty challenges?
Trans. Mach. Learn. Res.
Xi Wang, Eric T. Nalisnick
2026 J jnl
Conformal Thinking: Risk Control for Reasoning on a Compute Budget.
CoRR
Xi Wang, Anushri Suresh, Alvin Zhang, Rishi More, William Jurayj, Benjamin Van Durme, Mehrdad Farajtabar, Daniel Khashabi, Eric T. Nalisnick
2026 J jnl
Learning to Defer with an Uncertain Rejector via Conformal Prediction.
Trans. Mach. Learn. Res.
Yizirui Fang, Eric T. Nalisnick
2025 A* conf
Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence.
ICLR
Dharmesh Tailor, Alvaro H. C. Correia, Eric T. Nalisnick, Christos Louizos
2025 J jnl
Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence.
CoRR
Dharmesh Tailor, Alvaro H. C. Correia, Eric T. Nalisnick, Christos Louizos
2025 J jnl
Are vision language models robust to uncertain inputs?
CoRR
Xi Wang, Eric T. Nalisnick
2025 B conf
DefVerify: Do Hate Speech Models Reflect Their Dataset's Definition?
COLING
Urja Khurana, Eric T. Nalisnick, Antske Fokkens
2025 A* conf
ELBOing Stein: Variational Bayes with Stein Mixture Inference.
ICLR
Ola Rønning, Eric T. Nalisnick, Christophe Ley, Padhraic Smyth, Thomas Hamelryck
2025 A conf
Generative Uncertainty in Diffusion Models.
UAI
Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric T. Nalisnick, Stephan Mandt
2025 J jnl
Generative Uncertainty in Diffusion Models.
CoRR
Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric T. Nalisnick, Stephan Mandt
2025 A* conf
Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach.
EMNLP
Alessa Carbo, Eric T. Nalisnick
2025 J jnl
Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach.
CoRR
Alessa Carbo, Eric T. Nalisnick
2025 J jnl
Lightning UQ Box: Uncertainty Quantification for Neural Networks.
J. Mach. Learn. Res.
Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric T. Nalisnick
2025 A conf
Max-Rank: Efficient Multiple Testing for Conformal Prediction.
AISTATS
Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Christian A. Naesseth, Eric T. Nalisnick
2025 J jnl
Monitoring Risks in Test-Time Adaptation.
CoRR
Mona Schirmer, Metod Jazbec, Christian A. Naesseth, Eric T. Nalisnick
2025 — conf
On Calibration in Multi-Distribution Learning.
FAccT
Rajeev Verma, Volker Fischer, Eric T. Nalisnick
2025 A conf
On Continuous Monitoring of Risk Violations under Unknown Shift.
UAI
Alexander Timans, Rajeev Verma, Eric T. Nalisnick, Christian A. Naesseth
2025 J jnl
On Continuous Monitoring of Risk Violations under Unknown Shift.
CoRR
Alexander Timans, Rajeev Verma, Eric T. Nalisnick, Christian A. Naesseth
2025 J jnl
On the Challenges and Opportunities in Generative AI.
Trans. Mach. Learn. Res.
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 T. 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
2025 J jnl
Safe and Efficient In-Context Learning via Risk Control.
CoRR
Andrea Wynn, Metod Jazbec, Charith Peris, Rinat Khaziev, Anqi Liu, Daniel Khashabi, Eric T. Nalisnick
2025 J jnl
Scalable Generative Modeling of Weighted Graphs.
CoRR
Richard Williams, Eric T. Nalisnick, Andrew Holbrook
2025 J jnl
Scalable Generative Modeling of Weighted Graphs.
Trans. Mach. Learn. Res.
Richard Williams, Eric T. Nalisnick, Andrew Holbrook
2025 J jnl
Temporal Test-Time Adaptation with State-Space Models.
Trans. Mach. Learn. Res.
Mona Schirmer, Dan Zhang, Eric T. Nalisnick
2024 A* conf
A Generative Model of Symmetry Transformations.
NeurIPS
James Urquhart Allingham, Bruno Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024 J jnl
A Generative Model of Symmetry Transformations.
CoRR
James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024 — conf
Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction.
ECCV (88)
Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric T. Nalisnick
2024 J jnl
Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction.
CoRR
Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric T. Nalisnick
2024 J jnl
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?
CoRR
Urja Khurana, Eric T. Nalisnick, Antske Fokkens, Swabha Swayamdipta
2024 J jnl
DefVerify: Do Hate Speech Models Reflect Their Dataset's Definition?
CoRR
Urja Khurana, Eric T. Nalisnick, Antske Fokkens
2024 J jnl
ELBOing Stein: Variational Bayes with Stein Mixture Inference.
CoRR
Ola Rønning, Eric T. Nalisnick, Christophe Ley, Padhraic Smyth, Thomas Hamelryck
2024 A conf
Early-Exit Neural Networks with Nested Prediction Sets.
UAI
Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick
2024 A* conf
Fast yet Safe: Early-Exiting with Risk Control.
NeurIPS
Metod Jazbec, Alexander Timans, Tin Hadzi Veljkovic, Kaspar Sakmann, Dan Zhang, Christian Andersson Naesseth, Eric T. Nalisnick
2024 J jnl
Fast yet Safe: Early-Exiting with Risk Control.
CoRR
Metod Jazbec, Alexander Timans, Tin Hadzi Veljkovic, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric T. Nalisnick
2024 A conf
Learning to Defer to a Population: A Meta-Learning Approach.
AISTATS
Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric T. Nalisnick
2024 J jnl
Learning to Defer to a Population: A Meta-Learning Approach.
CoRR
Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric T. Nalisnick
2024 J jnl
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning.
CoRR
Nils Lehmann, Jakob Gawlikowski, Adam J. Stewart, Vytautas Jancauskas, Stefan Depeweg, Eric T. Nalisnick, Nina Maria Gottschling
2024 J jnl
On Calibration in Multi-Distribution Learning.
CoRR
Rajeev Verma, Volker Fischer, Eric T. Nalisnick
2024 J jnl
On the Challenges and Opportunities in Generative AI.
CoRR
Laura Manduchi, 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 T. 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
2024 J jnl
Practical Synthesis of Mixed-Tailed Data with Normalizing Flows.
Trans. Mach. Learn. Res.
Saba Amiri, Eric T. Nalisnick, Adam Belloum, Sander Klous, Leon Gommans
2024 J jnl
Test-Time Adaptation with State-Space Models.
CoRR
Mona Schirmer, Dan Zhang, Eric T. Nalisnick
2024 J jnl
Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data.
CoRR
Nils Lehmann, Nina Maria Gottschling, Stefan Depeweg, Eric T. Nalisnick
2023 J jnl
Active Learning for Multilingual Fingerspelling Corpora.
CoRR
Shuai Wang, Eric T. Nalisnick
2023 J jnl
Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks.
CoRR
Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick
2023 J jnl
Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift.
CoRR
Mona Schirmer, Dan Zhang, Eric T. Nalisnick
2023 A conf
Do Bayesian Neural Networks Need To Be Fully Stochastic?
AISTATS
Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth
2023 A conf
Exploiting Inferential Structure in Neural Processes.
UAI
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick
2023 J jnl
Exploiting Inferential Structure in Neural Processes.
CoRR
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick
2023 A conf
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles.
AISTATS
Rajeev Verma, Daniel Barrejón, Eric T. Nalisnick
2023 J jnl
One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images.
CoRR
Thomas Jurriaans, Kinga Szarkowska, Eric T. Nalisnick, Markus Schwörer, Camilo Thorne, Saber A. Akhondi
2023 A* conf
Sampling-based inference for large linear models, with application to linearised Laplace.
ICLR
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2023 A* conf
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity.
NeurIPS
Metod Jazbec, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick
2023 J jnl
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity.
CoRR
Metod Jazbec, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick
2022 A* conf
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.
ICML
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022 J jnl
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.
CoRR
Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022 J jnl
Adversarial Defense via Image Denoising with Chaotic Encryption.
CoRR
Shi Hu, Eric T. Nalisnick, Max Welling
2022 A* conf
Calibrated Learning to Defer with One-vs-All Classifiers.
ICML
Rajeev Verma, Eric T. Nalisnick
2022 J jnl
Calibrated Learning to Defer with One-vs-All Classifiers.
CoRR
Rajeev Verma, Eric T. Nalisnick
2022 J jnl
Do Bayesian Neural Networks Need To Be Fully Stochastic?
CoRR
Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth
2022 J jnl
Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions.
CoRR
Urja Khurana, Ivar E. Vermeulen, Eric T. Nalisnick, Marloes van Noorloos, Antske Fokkens
2022 J jnl
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles.
CoRR
Rajeev Verma, Daniel Barrejón, Eric T. Nalisnick
2022 — conf
On the impact of non-IID data on the performance and fairness of differentially private federated learning.
DSN Workshops
Saba Amiri, Adam Belloum, Eric T. Nalisnick, Sander Klous, Leon Gommans
2022 J jnl
Sampling-based inference for large linear models, with application to linearised Laplace.
CoRR
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2021 A* conf
Bayesian Deep Learning via Subnetwork Inference.
ICML
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021 — conf
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task.
Eval4NLP
Urja Khurana, Eric T. Nalisnick, Antske Fokkens
2021 J jnl
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task.
CoRR
Urja Khurana, Eric T. Nalisnick, Antske Fokkens
2021 J jnl
Normalizing Flows for Probabilistic Modeling and Inference.
J. Mach. Learn. Res.
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
2021 A conf
Predictive Complexity Priors.
AISTATS
Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato
2020 J jnl
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference.
CoRR
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2020 J jnl
Predictive Complexity Priors.
CoRR
Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato
2019 A* conf
Bayesian Batch Active Learning as Sparse Subset Approximation.
NeurIPS
Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato
2019 J jnl
Bayesian Batch Active Learning as Sparse Subset Approximation.
CoRR
Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato
2019 J jnl
Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality.
CoRR
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan
2019 — conf
Do Deep Generative Models Know What They Don't Know?
ICLR (Poster)
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019 A* conf
Dropout as a Structured Shrinkage Prior.
ICML
Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth
2019 A* conf
Hybrid Models with Deep and Invertible Features.
ICML
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019 J jnl
Hybrid Models with Deep and Invertible Features.
CoRR
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019 J jnl
Normalizing Flows for Probabilistic Modeling and Inference.
CoRR
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
2018 — conf
Bayesian Trees for Automated Cytometry Data Analysis.
MLHC
Disi Ji, Eric T. Nalisnick, Yu Qian, Richard H. Scheuermann, Padhraic Smyth
2018 J jnl
Do Deep Generative Models Know What They Don't Know?
CoRR
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2018 A conf
Learning Priors for Invariance.
AISTATS
Eric T. Nalisnick, Padhraic Smyth
2018 — —
On Priors for Bayesian Neural Networks.
Eric T. Nalisnick
2018 — conf
The Effectiveness of a two-Layer Neural Network for Recommendations.
ICLR (Workshop)
Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric T. Nalisnick, Leo Dirac, Runfei Luo
2018 J jnl
Unifying the Dropout Family Through Structured Shrinkage Priors.
CoRR
Eric T. Nalisnick, Padhraic Smyth
2017 A conf
Learning Approximately Objective Priors.
UAI
Eric T. Nalisnick, Padhraic Smyth
2017 — conf
Stick-Breaking Variational Autoencoders.
ICLR (Poster)
Eric T. Nalisnick, Padhraic Smyth
2017 — conf
Variational Reference Priors.
ICLR (Workshop)
Eric T. Nalisnick, Padhraic Smyth
2016 J jnl
A Dual Embedding Space Model for Document Ranking.
CoRR
Bhaskar Mitra, Eric T. Nalisnick, Nick Craswell, Rich Caruana
2016 — conf
Analyzing NIH Funding Patterns over Time with Statistical Text Analysis.
AAAI Workshop: Scholarly Big Data
Jihyun Park, Margaret Blume-Kohout, Ralf Krestel, Eric T. Nalisnick, Padhraic Smyth
2016 — conf
Improving Document Ranking with Dual Word Embeddings.
WWW (Companion Volume)
Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana
2015 J jnl
Infinite Dimensional Word Embeddings.
CoRR
Eric T. Nalisnick, Sachin Ravi
2013 — conf
Character-to-Character Sentiment Analysis in Shakespeare's Plays.
ACL (2)
Eric T. Nalisnick, Henry S. Baird
2013 A conf
Extracting Sentiment Networks from Shakespeare's Plays.
ICDAR
Eric T. Nalisnick, Henry S. Baird