An Empirical Comparison of Syllabuses for Curriculum Learning. - Mark Collier; Jöran Beel (AICS, 2018)

Implementing Neural Turing Machines. - Mark Collier; Jöran Beel (ICANN (3), 2018)

Deep Contextual Multi-armed Bandits. - Mark Collier; Hector Urdiales Llorens (CoRR, 2018)

Memory-Augmented Neural Networks for Machine Translation. - Mark Collier; Joeran Beel (MTSummit (1), 2019)

Scalable Deep Unsupervised Clustering with Concrete GMVAEs. - Mark Collier; Hector Urdiales (CoRR, 2019)

Analysis of Softmax Approximation for Deep Classifiers under Input-Dependent Label Noise. - Mark Collier; Basil Mustafa; Efi Kokiopoulou; Jesse Berent (CoRR, 2020)

VAEs in the Presence of Missing Data. - Mark Collier; Alfredo Nazábal; Christopher K. I. Williams (CoRR, 2020)

Routing Networks with Co-training for Continual Learning. - Mark Collier; Efi Kokiopoulou; Andrea Gesmundo; Jesse Berent (CoRR, 2020)

Correlated Input-Dependent Label Noise in Large-Scale Image Classification. - Mark Collier; Basil Mustafa; Efi Kokiopoulou; Rodolphe Jenatton; Jesse Berent (CVPR, 2021)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - Zachary Nado; Neil Band; Mark Collier; Josip Djolonga; Michael W. Dusenberry; Sebastian Farquhar; Angelos Filos; Marton Havasi; Rodolphe Jenatton; Ghassen Jerfel; Jeremiah Z. Liu; Zelda Mariet; et al. (CoRR, 2021)

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information. - Mark Collier; Rodolphe Jenatton; Effrosyni Kokiopoulou; Jesse Berent (ICML, 2022)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 2022)

Deep Classifiers with Label Noise Modeling and Distance Awareness. - Vincent Fortuin; Mark Collier; Florian Wenzel; James Urquhart Allingham; Jeremiah Zhe Liu; Dustin Tran; Balaji Lakshminarayanan; Jesse Berent; Rodolphe Jenatton; Effrosyni Kokiopoulou (Trans. Mach. Learn. Res., 2022)

Massively Scaling Heteroscedastic Classifiers. - Mark Collier; Rodolphe Jenatton; Basil Mustafa; Neil Houlsby; Jesse Berent; Effrosyni Kokiopoulou (ICLR, 2023)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

When does Privileged information Explain Away Label Noise? - Guillermo Ortiz-Jiménez; Mark Collier; Anant Nawalgaria; Alexander Nicholas D'Amour; Jesse Berent; Rodolphe Jenatton; Efi Kokiopoulou (ICML, 2023)

Three Towers: Flexible Contrastive Learning with Pretrained Image Models. - Jannik Kossen; Mark Collier; Basil Mustafa; Xiao Wang 038; Xiaohua Zhai; Lucas Beyer; Andreas Steiner 001; Jesse Berent; Rodolphe Jenatton; Effrosyni Kokiopoulou (NeurIPS, 2023)

Pi-DUAL: Using privileged information to distinguish clean from noisy labels. - Ke Wang; Guillermo Ortiz-Jiménez; Rodolphe Jenatton; Mark Collier; Efi Kokiopoulou; Pascal Frossard (ICML, 2024)

Representing Online Handwriting for Recognition in Large Vision-Language Models. - Anastasiia Fadeeva; Philippe Schlattner; Andrii Maksai; Mark Collier; Efi Kokiopoulou; Jesse Berent; Claudiu Musat (CoRR, 2024)

Pretrained Visual Uncertainties. - Michael Kirchhof; Mark Collier; Seong Joon Oh; Enkelejda Kasneci (CoRR, 2024)