van Amersfoort, Joost

7 publications

CVPR 2023 Deep Deterministic Uncertainty: A New Simple Baseline Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H.S. Torr, Yarin Gal

ICLRW 2023 DyNeMoC: A Semi-Supervised Architecture for Classifying Time Series Brain Data Abu Mohammad Shabbir Khan, Chetan Gohil, Pascal Notin, Joost van Amersfoort, Mark Woolrich, Yarin Gal

ICMLW 2022 Plex: Towards Reliability Using Pretrained Large Model Extensions Dustin Tran, Jeremiah Zhe Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda E Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, E. Kelly Buchanan, Kevin Patrick Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

ICLR 2022 Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal

NeurIPS 2021 Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal

ICML 2020 Uncertainty Estimation Using a Single Deep Deterministic Neural Network Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal

NeurIPS 2019 BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning Andreas Kirsch, Joost van Amersfoort, Yarin Gal