Interlocking Backpropagation: Improving depthwise model-parallelism. - Aidan N. Gomez; Oscar Key; Stephen Gou; Nick Frosst; Jeff Dean; Yarin Gal (CoRR, 2020)

Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. - Lisa Schut; Oscar Key; Rory McGrath; Luca Costabello; Bogdan Sacaleanu; Medb Corcoran; Yarin Gal (AISTATS, 2021)

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. - Tim G. J. Rudner; Oscar Key; Yarin Gal; Tom Rainforth (ICML, 2021)

Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. - Joost van Amersfoort; Lewis Smith; Andrew Jesson; Oscar Key; Yarin Gal (CoRR, 2021)

Composite Goodness-of-fit Tests with Kernels. - Oscar Key; Tamara Fernandez; Arthur Gretton; François-Xavier Briol (CoRR, 2021)

Towards Healing the Blindness of Score Matching. - Mingtian Zhang; Oscar Key; Peter Hayes; David Barber; Brooks Paige; François-Xavier Briol (CoRR, 2022)

Interlocking Backpropagation: Improving depthwise model-parallelism. - Aidan N. Gomez; Oscar Key; Kuba Perlin; Stephen Gou; Nick Frosst; Jeff Dean; Yarin Gal (J. Mach. Learn. Res., 2022)

Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference. - Ayush Bharti; Masha Naslidnyk; Oscar Key; Samuel Kaski; François-Xavier Briol (ICML, 2023)

No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. - Jean Kaddour; Oscar Key; Piotr Nawrot; Pasquale Minervini; Matt J. Kusner (NeurIPS, 2023)

Scalable Data Assimilation with Message Passing. - Oscar Key; So Takao; Daniel Giles; Marc Peter Deisenroth (CoRR, 2024)

Approximate Top-k for Increased Parallelism. - Oscar Key; Luka Ribar; Alberto Cattaneo; Luke Hudlass-Galley; Douglas Orr (CoRR, 2024)