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ORCIDEric T. Nalisnick
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Books and Theses
- 2018
[b1]Eric T. Nalisnick:
On Priors for Bayesian Neural Networks. University of California, Irvine, USA, 2018
Journal Articles
- 2021
- [j1]George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan:
Normalizing Flows for Probabilistic Modeling and Inference. J. Mach. Learn. Res. 22: 57:1-57:64 (2021)
Conference and Workshop Papers
- 2024
- [c26]Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric T. Nalisnick:
Learning to Defer to a Population: A Meta-Learning Approach. AISTATS 2024: 3475-3483 - 2023
- [c25]Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth:
Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023: 7694-7722 - [c24]Rajeev Verma, Daniel Barrejón, Eric T. Nalisnick:
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles. AISTATS 2023: 11415-11434 - [c23]Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato:
Sampling-based inference for large linear models, with application to linearised Laplace. ICLR 2023 - [c22]Metod Jazbec, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick:
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. NeurIPS 2023 - [c21]Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick:
Exploiting Inferential Structure in Neural Processes. UAI 2023: 2089-2098 - 2022
- [c20]Saba Amiri, Adam Belloum, Eric T. Nalisnick, Sander Klous, Leon Gommans:
On the impact of non-IID data on the performance and fairness of differentially private federated learning. DSN Workshops 2022: 52-58 - [c19]Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML 2022: 796-821 - [c18]Rajeev Verma, Eric T. Nalisnick:
Calibrated Learning to Defer with One-vs-All Classifiers. ICML 2022: 22184-22202 - 2021
- [c17]Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato:
Predictive Complexity Priors. AISTATS 2021: 694-702 - [c16]Urja Khurana, Eric T. Nalisnick, Antske Fokkens:
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task. Eval4NLP 2021: 16-31 - [c15]Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Bayesian Deep Learning via Subnetwork Inference. ICML 2021: 2510-2521 - 2019
- [c14]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Do Deep Generative Models Know What They Don't Know? ICLR (Poster) 2019 - [c13]Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth:
Dropout as a Structured Shrinkage Prior. ICML 2019: 4712-4722 - [c12]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Hybrid Models with Deep and Invertible Features. ICML 2019: 4723-4732 - [c11]Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Bayesian Batch Active Learning as Sparse Subset Approximation. NeurIPS 2019: 6356-6367 - 2018
- [c10]Eric T. Nalisnick, Padhraic Smyth:
Learning Priors for Invariance. AISTATS 2018: 366-375 - [c9]Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric T. Nalisnick, Leo Dirac, Runfei Luo:
The Effectiveness of a two-Layer Neural Network for Recommendations. ICLR (Workshop) 2018 - [c8]Disi Ji, Eric T. Nalisnick, Yu Qian, Richard H. Scheuermann, Padhraic Smyth:
Bayesian Trees for Automated Cytometry Data Analysis. MLHC 2018: 465-483 - 2017
- [c7]Eric T. Nalisnick, Padhraic Smyth:
Stick-Breaking Variational Autoencoders. ICLR (Poster) 2017 - [c6]Eric T. Nalisnick, Padhraic Smyth:
Variational Reference Priors. ICLR (Workshop) 2017 - [c5]Eric T. Nalisnick, Padhraic Smyth:
Learning Approximately Objective Priors. UAI 2017 - 2016
- [c4]Jihyun Park, Margaret Blume-Kohout, Ralf Krestel, Eric T. Nalisnick, Padhraic Smyth:
Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. AAAI Workshop: Scholarly Big Data 2016 - [c3]Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana:
Improving Document Ranking with Dual Word Embeddings. WWW (Companion Volume) 2016: 83-84 - 2013
- [c2]Eric T. Nalisnick, Henry S. Baird:
Character-to-Character Sentiment Analysis in Shakespeare's Plays. ACL (2) 2013: 479-483 - [c1]Eric T. Nalisnick, Henry S. Baird:
Extracting Sentiment Networks from Shakespeare's Plays. ICDAR 2013: 758-762
Informal and Other Publications
- 2024
- [i33]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:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i32]James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato:
A Generative Model of Symmetry Transformations. CoRR abs/2403.01946 (2024) - [i31]Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric T. Nalisnick:
Learning to Defer to a Population: A Meta-Learning Approach. CoRR abs/2403.02683 (2024) - [i30]Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Eric T. Nalisnick:
Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction. CoRR abs/2403.07263 (2024) - [i29]Nils Lehmann, Nina Maria Gottschling, Stefan Depeweg, Eric T. Nalisnick:
Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data. CoRR abs/2404.08325 (2024) - [i28]Metod Jazbec, Alexander Timans, Tin Hadzi Veljkovic, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric T. Nalisnick:
Fast yet Safe: Early-Exiting with Risk Control. CoRR abs/2405.20915 (2024) - [i27]Mona Schirmer, Dan Zhang, Eric T. Nalisnick:
Test-Time Adaptation with State-Space Models. CoRR abs/2407.12492 (2024) - [i26]Urja Khurana, Eric T. Nalisnick, Antske Fokkens, Swabha Swayamdipta:
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks? CoRR abs/2408.14141 (2024) - [i25]Nils Lehmann, Jakob Gawlikowski, Adam J. Stewart, Vytautas Jancauskas, Stefan Depeweg, Eric T. Nalisnick, Nina Maria Gottschling:
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning. CoRR abs/2410.03390 (2024) - 2023
- [i24]Metod Jazbec, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick:
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. CoRR abs/2306.02652 (2023) - [i23]Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick:
Exploiting Inferential Structure in Neural Processes. CoRR abs/2306.15169 (2023) - [i22]Shuai Wang, Eric T. Nalisnick:
Active Learning for Multilingual Fingerspelling Corpora. CoRR abs/2309.12443 (2023) - [i21]Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick:
Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks. CoRR abs/2311.05931 (2023) - [i20]Thomas Jurriaans, Kinga Szarkowska, Eric T. Nalisnick, Markus Schwörer, Camilo Thorne, Saber A. Akhondi:
One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images. CoRR abs/2311.14633 (2023) - [i19]Mona Schirmer, Dan Zhang, Eric T. Nalisnick:
Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift. CoRR abs/2312.08033 (2023) - 2022
- [i18]Rajeev Verma, Eric T. Nalisnick:
Calibrated Learning to Defer with One-vs-All Classifiers. CoRR abs/2202.03673 (2022) - [i17]Shi Hu, Eric T. Nalisnick, Max Welling:
Adversarial Defense via Image Denoising with Chaotic Encryption. CoRR abs/2203.10290 (2022) - [i16]Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR abs/2206.08900 (2022) - [i15]Urja Khurana, Ivar E. Vermeulen, Eric T. Nalisnick, Marloes van Noorloos, Antske Fokkens:
Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions. CoRR abs/2206.15455 (2022) - [i14]Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato:
Sampling-based inference for large linear models, with application to linearised Laplace. CoRR abs/2210.04994 (2022) - [i13]Rajeev Verma, Daniel Barrejón
, Eric T. Nalisnick:
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles. CoRR abs/2210.16955 (2022) - [i12]Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth:
Do Bayesian Neural Networks Need To Be Fully Stochastic? CoRR abs/2211.06291 (2022) - 2021
- [i11]Urja Khurana, Eric T. Nalisnick, Antske Fokkens:
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task. CoRR abs/2111.09612 (2021) - 2020
- [i10]Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato:
Predictive Complexity Priors. CoRR abs/2006.10801 (2020) - [i9]Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. CoRR abs/2010.14689 (2020) - 2019
- [i8]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Hybrid Models with Deep and Invertible Features. CoRR abs/1902.02767 (2019) - [i7]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan:
Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality. CoRR abs/1906.02994 (2019) - [i6]Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Bayesian Batch Active Learning as Sparse Subset Approximation. CoRR abs/1908.02144 (2019) - [i5]George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan:
Normalizing Flows for Probabilistic Modeling and Inference. CoRR abs/1912.02762 (2019) - 2018
- [i4]Eric T. Nalisnick, Padhraic Smyth:
Unifying the Dropout Family Through Structured Shrinkage Priors. CoRR abs/1810.04045 (2018) - [i3]Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Do Deep Generative Models Know What They Don't Know? CoRR abs/1810.09136 (2018) - 2016
- [i2]Bhaskar Mitra, Eric T. Nalisnick, Nick Craswell, Rich Caruana:
A Dual Embedding Space Model for Document Ranking. CoRR abs/1602.01137 (2016) - 2015
- [i1]Eric T. Nalisnick, Sachin Ravi:
Infinite Dimensional Word Embeddings. CoRR abs/1511.05392 (2015)
Coauthor Index
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