Jeremiah Liu
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Pushing the Accuracy-Group Robustness Tradeoff Frontier with Introspective Self-play
Dj Dvijotham
Jihyeon Lee
Martin Strobel
Quan Yuan
ICLR'23 (2023) (to appear)
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Shreyas Padhy
Zi Lin
Yeming Wen
Ghassen Jerfel
Journal of Machine Learning Research (2022)
Plex: Towards Reliability using Pretrained Large Model Extensions
Du Phan
Mark Patrick Collier
Zi Wang
Zelda Mariet
Clara Huiyi Hu
Neil Band
Tim G. J. Rudner
Karan Singhal
Joost van Amersfoort
Andreas Christian Kirsch
Rodolphe Jenatton
Honglin Yuan
Kelly Buchanan
D. Sculley
Yarin Gal
ICML 2022 Pre-training Workshop (2022)
Variable Selection with Rigorous Uncertainty Quantification using Bayesian Deep Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
International Conference on Artificial Intelligence and Statistics, PMLR (2021)
Deep Classifiers with Label Noise Modeling and Distance Awareness
Vincent Fortuin
Mark Patrick Collier
Florian Wenzel
James Urquhart Allingham
Jesse Berent
Rodolphe Jenatton
NeurIPS 2021 Workshop on Bayesian Deep Learning (2021) (to appear)
Training independent subnetworks for robust prediction
Marton Havasi
Rodolphe Jenatton
Stanislav Fort
International Conference on Learning Representations (2021)
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Zi Lin
Shreyas Padhy
Advances in Neural Information Processing Systems 33, Curran Associates, Inc. (2020) (to appear)
