Eric T. Nalisnick

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 44

Top Venues

CoRR

ICML

AISTATS

ICLR (Workshop)

University of California, Irvine, USA (PHD)

Education History

University of California, Irvine, USA

PHD

Publications

Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick
Exploiting Inferential Structure in Neural Processes. UAI (2023)
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick
Exploiting Inferential Structure in Neural Processes. CoRR (2023)
Shuai Wang, Eric T. Nalisnick
Active Learning for Multilingual Fingerspelling Corpora. CoRR (2023)
Rajeev Verma, Daniel Barrejón, Eric T. Nalisnick
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles. AISTATS (2023)
Metod Jazbec, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. NeurIPS (2023)
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 (2023)
Mona Schirmer, Dan Zhang, Eric T. Nalisnick
Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift. CoRR (2023)
Metod Jazbec, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. CoRR (2023)
Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth
Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS (2023)
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)
Rajeev Verma, Eric T. Nalisnick
Calibrated Learning to Defer with One-vs-All Classifiers. CoRR (2022)
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 (2022)
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 (2022)
Rajeev Verma, Eric T. Nalisnick
Calibrated Learning to Defer with One-vs-All Classifiers. ICML (2022)
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 (2022)
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)
Shi Hu, Eric T. Nalisnick, Max Welling
Adversarial Defense via Image Denoising with Chaotic Encryption. CoRR (2022)
Rajeev Verma, Daniel Barrejón, Eric T. Nalisnick
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles. CoRR (2022)
Urja Khurana, Eric T. Nalisnick, Antske Fokkens
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task. CoRR (2021)
Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Bayesian Deep Learning via Subnetwork Inference. ICML (2021)
Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato
Predictive Complexity Priors. AISTATS (2021)
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
Normalizing Flows for Probabilistic Modeling and Inference. J. Mach. Learn. Res. 22 (2021)
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 (2020)
Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato
Predictive Complexity Priors. CoRR (2020)
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 (2019)
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)
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
Normalizing Flows for Probabilistic Modeling and Inference. CoRR (2019)
Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato
Bayesian Batch Active Learning as Sparse Subset Approximation. NeurIPS (2019)
Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth
Dropout as a Structured Shrinkage Prior. ICML (2019)
Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato
Bayesian Batch Active Learning as Sparse Subset Approximation. CoRR (2019)
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
Hybrid Models with Deep and Invertible Features. CoRR (2019)
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
Hybrid Models with Deep and Invertible Features. ICML (2019)
Eric T. Nalisnick, Padhraic Smyth
Unifying the Dropout Family Through Structured Shrinkage Priors. CoRR (2018)
Disi Ji, Eric T. Nalisnick, Yu Qian, Richard H. Scheuermann, Padhraic Smyth
Bayesian Trees for Automated Cytometry Data Analysis. MLHC (2018)
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
Do Deep Generative Models Know What They Don't Know? CoRR (2018)
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)
Eric T. Nalisnick, Padhraic Smyth
Learning Priors for Invariance. AISTATS (2018)
Eric T. Nalisnick, Padhraic Smyth
Variational Reference Priors. ICLR (Workshop) (2017)
Eric T. Nalisnick, Padhraic Smyth
Stick-Breaking Variational Autoencoders. ICLR (Poster) (2017)
Eric T. Nalisnick, Padhraic Smyth
Learning Approximately Objective Priors. UAI (2017)
Bhaskar Mitra, Eric T. Nalisnick, Nick Craswell, Rich Caruana
A Dual Embedding Space Model for Document Ranking. CoRR (2016)
Eric T. Nalisnick, Bhaskar Mitra, Nick Craswell, Rich Caruana
Improving Document Ranking with Dual Word Embeddings. WWW (Companion Volume) (2016)
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)
Eric T. Nalisnick, Sachin Ravi
Infinite Dimensional Word Embeddings. CoRR (2015)
Eric T. Nalisnick, Henry S. Baird
Extracting Sentiment Networks from Shakespeare's Plays. ICDAR (2013)
Eric T. Nalisnick, Henry S. Baird
Character-to-Character Sentiment Analysis in Shakespeare's Plays. ACL (2) (2013)

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Publication Activity (10 Years)

Top Topics

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Eric T. Nalisnick