Deep Kernel Processes. - Laurence Aitchison; Adam X. Yang; Sebastian W. Ober (ICML, 2021)

A theory of representation learning gives a deep generalisation of kernel methods. - Adam X. Yang; Maxime Robeyns; Edward Milsom; Ben Anson; Nandi Schoots; Laurence Aitchison (ICML, 2023)

MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning. - Adam X. Yang; Laurence Aitchison; Henry B. Moss (CoRR, 2023)

Bayesian Low-rank Adaptation for Large Language Models. - Adam X. Yang; Maxime Robeyns; Xi Wang; Laurence Aitchison (ICLR, 2024)

Instruction Tuning With Loss Over Instructions. - Zhengxiang Shi; Adam X. Yang; Bin Wu 025; Laurence Aitchison; Emine Yilmaz; Aldo Lipani (NeurIPS, 2024)

Bayesian Reward Models for LLM Alignment. - Adam X. Yang; Maxime Robeyns; Thomas Coste; Jun Wang 012; Haitham Bou-Ammar; Laurence Aitchison (CoRR, 2024)

Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries. - Adam Yang 002; Chen Chen; Konstantinos Pitas (CoRR, 2024)

Instruction Tuning With Loss Over Instructions. - Zhengyan Shi; Adam X. Yang; Bin Wu 025; Laurence Aitchison; Emine Yilmaz; Aldo Lipani (CoRR, 2024)