Eric T. Nalisnick

Antske Fokkens

CoRR, 2024

Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning.

[BibT_eX]

[DOI]

Nina Maria Gottschling

CoRR, 2024

Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?

[BibT_eX]

[DOI]

CoRR, 2024

Test-Time Adaptation with State-Space Models.

[BibT_eX]

[DOI]

Mona Schirmer

Dan Zhang

CoRR, 2024

Fast yet Safe: Early-Exiting with Risk Control.

[BibT_eX]

[DOI]

Christian A. Naesseth

CoRR, 2024

Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data.

[BibT_eX]

[DOI]

Nils Lehmann

Nina Maria Gottschling

Stefan Depeweg

CoRR, 2024

A Generative Model of Symmetry Transformations.

[BibT_eX]

[DOI]

Bruno Kacper Mlodozeniec

Christoph-Nikolas Straehle

CoRR, 2024

On the Challenges and Opportunities in Generative AI.

[BibT_eX]

[DOI]

CoRR, 2024

Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction.

[BibT_eX]

[DOI]

Alexander Timans

Kaspar Sakmann

Proceedings of the Computer Vision - ECCV 2024, 2024

Learning to Defer to a Population: A Meta-Learning Approach.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift.

[BibT_eX]

[DOI]

Mona Schirmer

Dan Zhang

CoRR, 2023

One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images.

[BibT_eX]

[DOI]

CoRR, 2023

Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Active Learning for Multilingual Fingerspelling Corpora.

[BibT_eX]

[DOI]

Shuai Wang

CoRR, 2023

Exploiting Inferential Structure in Neural Processes.

[BibT_eX]

[DOI]

Dharmesh Tailor

Mohammad Emtiyaz Khan

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity.

[BibT_eX]

[DOI]

Metod Jazbec

Dan Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sampling-based inference for large linear models, with application to linearised Laplace.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles.

[BibT_eX]

[DOI]

Rajeev Verma

Daniel Barrejón

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Do Bayesian Neural Networks Need To Be Fully Stochastic?

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions.

[BibT_eX]

[DOI]

CoRR, 2022

Adversarial Defense via Image Denoising with Chaotic Encryption.

[BibT_eX]

[DOI]

Shi Hu

Max Welling

CoRR, 2022

Calibrated Learning to Defer with One-vs-All Classifiers.

[BibT_eX]

[DOI]

Rajeev Verma

Proceedings of the International Conference on Machine Learning, 2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.

[BibT_eX]

[DOI]

Javier Antorán

David Janz

Erik A. Daxberger

Riccardo Barbano

Proceedings of the International Conference on Machine Learning, 2022

On the impact of non-IID data on the performance and fairness of differentially private federated learning.

[BibT_eX]

[DOI]

Proceedings of the 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2022

2021

Normalizing Flows for Probabilistic Modeling and Inference.

[BibT_eX]

[DOI]

George Papamakarios

Danilo Jimenez Rezende

Shakir Mohamed

J. Mach. Learn. Res., 2021

Bayesian Deep Learning via Subnetwork Inference.

[BibT_eX]

[DOI]

Erik A. Daxberger

Javier Antorán

Proceedings of the 38th International Conference on Machine Learning, 2021

How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task.

[BibT_eX]

[DOI]

Urja Khurana

Antske Fokkens

Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems, 2021

Predictive Complexity Priors.

[BibT_eX]

[DOI]

Jonathan Gordon

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference.

[BibT_eX]

[DOI]

Erik A. Daxberger

Javier Antorán

CoRR, 2020

2019

Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality.

[BibT_eX]

[DOI]

Akihiro Matsukawa

Yee Whye Teh

CoRR, 2019

Bayesian Batch Active Learning as Sparse Subset Approximation.

[BibT_eX]

[DOI]

Robert Pinsler

Jonathan Gordon

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Hybrid Models with Deep and Invertible Features.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Dropout as a Structured Shrinkage Prior.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Do Deep Generative Models Know What They Don't Know?

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

On Priors for Bayesian Neural Networks.

[BibT_eX]

[DOI]

PhD thesis, 2018

Unifying the Dropout Family Through Structured Shrinkage Priors.

[BibT_eX]

[DOI]

CoRR, 2018

Bayesian Trees for Automated Cytometry Data Analysis.

[BibT_eX]

[DOI]

Disi Ji

Yu Qian

Richard H. Scheuermann

Proceedings of the Machine Learning for Healthcare Conference, 2018

The Effectiveness of a two-Layer Neural Network for Recommendations.

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Priors for Invariance.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Learning Approximately Objective Priors.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Variational Reference Priors.

[BibT_eX]

[DOI]

Proceedings of the 5th International Conference on Learning Representations, 2017

Stick-Breaking Variational Autoencoders.

[BibT_eX]

[DOI]

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

A Dual Embedding Space Model for Document Ranking.

[BibT_eX]

[DOI]

CoRR, 2016

Improving Document Ranking with Dual Word Embeddings.

[BibT_eX]

[DOI]

Proceedings of the 25th International Conference on World Wide Web, 2016

Analyzing NIH Funding Patterns over Time with Statistical Text Analysis.

[BibT_eX]

[DOI]

Jihyun Park

Margaret Blume-Kohout

Ralf Krestel

Proceedings of the Scholarly Big Data: AI Perspectives, 2016

2015

Infinite Dimensional Word Embeddings.

[BibT_eX]

[DOI]

Sachin Ravi

CoRR, 2015

2013

Extracting Sentiment Networks from Shakespeare's Plays.

[BibT_eX]

[DOI]

Henry S. Baird

Proceedings of the 12th International Conference on Document Analysis and Recognition, 2013

Character-to-Character Sentiment Analysis in Shakespeare's Plays.

[BibT_eX]

[DOI]