Character-to-Character Sentiment Analysis in Shakespeare's Plays. - Eric T. Nalisnick; Henry S. Baird (ACL (2), 2013)

Extracting Sentiment Networks from Shakespeare's Plays. - Eric T. Nalisnick; Henry S. Baird (ICDAR, 2013)

Infinite Dimensional Word Embeddings. - Eric T. Nalisnick; Sachin Ravi (CoRR, 2015)

Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. - Jihyun Park; Margaret Blume-Kohout; Ralf Krestel; Eric T. Nalisnick; Padhraic Smyth (AAAI Workshop: Scholarly Big Data, 2016)

Improving Document Ranking with Dual Word Embeddings. - Eric T. Nalisnick; Bhaskar Mitra 001; Nick Craswell; Rich Caruana (WWW (Companion Volume), 2016)

A Dual Embedding Space Model for Document Ranking. - Bhaskar Mitra 001; Eric T. Nalisnick; Nick Craswell; Rich Caruana (CoRR, 2016)

Stick-Breaking Variational Autoencoders. - Eric T. Nalisnick; Padhraic Smyth (ICLR (Poster), 2017)

Variational Reference Priors. - Eric T. Nalisnick; Padhraic Smyth (ICLR (Workshop), 2017)

Learning Approximately Objective Priors. - Eric T. Nalisnick; Padhraic Smyth (UAI, 2017)

Learning Priors for Invariance. - Eric T. Nalisnick; Padhraic Smyth (AISTATS, 2018)

The Effectiveness of a two-Layer Neural Network for Recommendations. - Oleg Rybakov; Vijai Mohan; Avishkar Misra; Scott LeGrand; Rejith Joseph; Kiuk Chung; Siddharth Singh; Qian You; Eric T. Nalisnick; Leo Dirac; Runfei Luo (ICLR (Workshop), 2018)

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

Unifying the Dropout Family Through Structured Shrinkage Priors. - Eric T. Nalisnick; Padhraic Smyth (CoRR, 2018)

On Priors for Bayesian Neural Networks. - Eric T. Nalisnick (2018)

Do Deep Generative Models Know What They Don't Know? - Eric T. Nalisnick; Akihiro Matsukawa; Yee Whye Teh; Dilan Görür; Balaji Lakshminarayanan (ICLR (Poster), 2019)

Dropout as a Structured Shrinkage Prior. - Eric T. Nalisnick; José Miguel Hernández-Lobato; Padhraic Smyth (ICML, 2019)

Hybrid Models with Deep and Invertible Features. - Eric T. Nalisnick; Akihiro Matsukawa; Yee Whye Teh; Dilan Görür; Balaji Lakshminarayanan (ICML, 2019)

Bayesian Batch Active Learning as Sparse Subset Approximation. - Robert Pinsler; Jonathan Gordon 003; Eric T. Nalisnick; José Miguel Hernández-Lobato (NeurIPS, 2019)

Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality. - Eric T. Nalisnick; Akihiro Matsukawa; Yee Whye Teh; Balaji Lakshminarayanan (CoRR, 2019)

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. - Erik A. Daxberger; Eric T. Nalisnick; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2020)

Predictive Complexity Priors. - Eric T. Nalisnick; Jonathan Gordon 003; José Miguel Hernández-Lobato (AISTATS, 2021)

How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task. - Urja Khurana; Eric T. Nalisnick; Antske Fokkens (Eval4NLP, 2021)

Bayesian Deep Learning via Subnetwork Inference. - Erik A. Daxberger; Eric T. Nalisnick; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (ICML, 2021)

Normalizing Flows for Probabilistic Modeling and Inference. - George Papamakarios; Eric T. Nalisnick; Danilo Jimenez Rezende; Shakir Mohamed; Balaji Lakshminarayanan (J. Mach. Learn. Res., 2021)

On the impact of non-IID data on the performance and fairness of differentially private federated learning. - Saba Amiri; Adam Belloum; Eric T. Nalisnick; Sander Klous; Leon Gommans (DSN Workshops, 2022)

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. - Javier Antorán; David Janz; James Urquhart Allingham; Erik A. Daxberger; Riccardo Barbano; Eric T. Nalisnick; José Miguel Hernández-Lobato (ICML, 2022)

Calibrated Learning to Defer with One-vs-All Classifiers. - Rajeev Verma; Eric T. Nalisnick (ICML, 2022)

Adversarial Defense via Image Denoising with Chaotic Encryption. - Shi Hu; Eric T. Nalisnick; Max Welling (CoRR, 2022)

Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions. - Urja Khurana; Ivar E. Vermeulen; Eric T. Nalisnick; Marloes van Noorloos; Antske Fokkens (CoRR, 2022)

Do Bayesian Neural Networks Need To Be Fully Stochastic? - Mrinank Sharma; Sebastian Farquhar; Eric T. Nalisnick; Tom Rainforth (AISTATS, 2023)

Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles. - Rajeev Verma; Daniel Barrejón; Eric T. Nalisnick (AISTATS, 2023)

Sampling-based inference for large linear models, with application to linearised Laplace. - Javier Antorán; Shreyas Padhy; Riccardo Barbano; Eric T. Nalisnick; David Janz; José Miguel Hernández-Lobato (ICLR, 2023)

Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. - Metod Jazbec; James Urquhart Allingham; Dan Zhang; Eric T. Nalisnick (NeurIPS, 2023)

Exploiting Inferential Structure in Neural Processes. - Dharmesh Tailor; Mohammad Emtiyaz Khan; Eric T. Nalisnick (UAI, 2023)

Active Learning for Multilingual Fingerspelling Corpora. - Shuai Wang; Eric T. Nalisnick (CoRR, 2023)

Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks. - Metod Jazbec; Patrick Forré; Stephan Mandt; Dan Zhang; Eric T. Nalisnick (CoRR, 2023)

One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images. - Thomas Jurriaans; Kinga Szarkowska; Eric T. Nalisnick; Markus Schwörer; Camilo Thorne; Saber A. Akhondi (CoRR, 2023)

Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift. - Mona Schirmer; Dan Zhang; Eric T. Nalisnick (CoRR, 2023)

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

Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction. - Alexander Timans; Christoph-Nikolas Straehle; Kaspar Sakmann; Eric T. Nalisnick (ECCV (88), 2024)

A Generative Model of Symmetry Transformations. - James Urquhart Allingham; Bruno Mlodozeniec; Shreyas Padhy; Javier Antorán; David Krueger 001; Richard E. Turner; Eric T. Nalisnick; José Miguel Hernández-Lobato (NeurIPS, 2024)

Fast yet Safe: Early-Exiting with Risk Control. - Metod Jazbec; Alexander Timans; Tin Hadzi Veljkovic; Kaspar Sakmann; Dan Zhang; Christian Andersson Naesseth; Eric T. Nalisnick (NeurIPS, 2024)

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

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data. - Nils Lehmann; Nina Maria Gottschling; Stefan Depeweg; Eric T. Nalisnick (CoRR, 2024)

Test-Time Adaptation with State-Space Models. - Mona Schirmer; Dan Zhang; Eric T. Nalisnick (CoRR, 2024)

Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks? - Urja Khurana; Eric T. Nalisnick; Antske Fokkens; Swabha Swayamdipta (CoRR, 2024)

Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning. - Nils Lehmann; Jakob Gawlikowski; Adam J. Stewart; Vytautas Jancauskas; Stefan Depeweg; Eric T. Nalisnick; Nina Maria Gottschling (CoRR, 2024)

On Calibration in Multi-Distribution Learning. - Rajeev Verma; Volker Fischer; Eric T. Nalisnick (CoRR, 2024)

Practical Synthesis of Mixed-Tailed Data with Normalizing Flows. - Saba Amiri; Eric T. Nalisnick; Adam Belloum; Sander Klous; Leon Gommans (Trans. Mach. Learn. Res., 2024)

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

DefVerify: Do Hate Speech Models Reflect Their Dataset's Definition? - Urja Khurana; Eric T. Nalisnick; Antske Fokkens (COLING, 2025)

On Calibration in Multi-Distribution Learning. - Rajeev Verma; Volker Fischer 003; Eric T. Nalisnick (FAccT, 2025)

ELBOing Stein: Variational Bayes with Stein Mixture Inference. - Ola Rønning; Eric T. Nalisnick; Christophe Ley; Padhraic Smyth; Thomas Hamelryck (ICLR, 2025)

Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence. - Dharmesh Tailor; Alvaro H. C. Correia; Eric T. Nalisnick; Christos Louizos (ICLR, 2025)

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

On Continuous Monitoring of Risk Violations under Unknown Shift. - Alexander Timans; Rajeev Verma; Eric T. Nalisnick; Christian A. Naesseth (UAI, 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 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)

Are vision language models robust to uncertain inputs? - Xi Wang; Eric T. Nalisnick (CoRR, 2025)

Monitoring Risks in Test-Time Adaptation. - Mona Schirmer; Metod Jazbec; Christian A. Naesseth; Eric T. Nalisnick (CoRR, 2025)

Scalable Generative Modeling of Weighted Graphs. - Richard Williams; Eric T. Nalisnick; Andrew Holbrook (CoRR, 2025)