James Urquhart Allingham

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 19

Top Topics

Selective Sampling

Top Venues

Trans. Mach. Learn. Res.

Publications

James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
A Generative Model of Symmetry Transformations. CoRR (2024)
James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Jeremiah Zhe Liu, Xiuye Gu, Yin Cui, Dustin Tran, Balaji Lakshminarayanan
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. CoRR (2023)
James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. ICML (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, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. CoRR (2023)
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)
James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (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)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. CoRR (2021)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Depth Uncertainty Networks for Active Learning. 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)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR (2021)
James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton
Sparse MoEs meet Efficient Ensembles. CoRR (2021)
Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. ICBINB@NeurIPS (2021)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Depth Uncertainty in Neural Networks. NeurIPS (2020)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Depth Uncertainty in Neural Networks. CoRR (2020)
Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
Variational Depth Search in ResNets. CoRR (2020)
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)