Spike-inspired rank coding for fast and accurate recurrent neural networks. - Alan Jeffares; Qinghai Guo; Pontus Stenetorp; Timoleon Moraitis (ICLR, 2022)

Improving Adaptive Conformal Prediction Using Self-Supervised Learning. - Nabeel Seedat; Alan Jeffares; Fergus Imrie; Mihaela van der Schaar (AISTATS, 2023)

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. - Alan Jeffares; Tennison Liu; Jonathan Crabbé; Fergus Imrie; Mihaela van der Schaar (ICLR, 2023)

A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. - Alicia Curth; Alan Jeffares; Mihaela van der Schaar (NeurIPS, 2023)

Joint Training of Deep Ensembles Fails Due to Learner Collusion. - Alan Jeffares; Tennison Liu; Jonathan Crabbé; Mihaela van der Schaar (NeurIPS, 2023)

Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise. - Thomas Pouplin; Alan Jeffares; Nabeel Seedat; Mihaela van der Schaar (ICML, 2024)

Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. - Alan Jeffares; Alicia Curth; Mihaela van der Schaar (NeurIPS, 2024)

Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. - Alicia Curth; Alan Jeffares; Mihaela van der Schaar (CoRR, 2024)

Partition Tree Ensembles for Improving Multi-Class Classification. - Miran Özdogan; Alan Jeffares; Sean B. Holden (ICPRAM, 2025)

An Introduction to Discrete Variational Autoencoders. - Alan Jeffares; Liyuan Liu (CoRR, 2025)

Not All Explanations for Deep Learning Phenomena Are Equally Valuable. - Alan Jeffares; Mihaela van der Schaar (CoRR, 2025)