54 citations · 63 across the 8 of their papers we have counts for
8 papers
Reasoning Arena: Trace Tournaments When Verifiable Rewards Fall Short
Han Zhou, Adam X. Yang, Laurence Aitchison +2
Reinforcement learning with verifiable rewards (RLVR) has become a leading paradigm for improving the reasoning ability of large language models through outcome-based supervision.…
Instruction Tuning With Loss Over Instructions
Zhengyan Shi, Adam X. Yang, Bin Wu +3
Instruction tuning plays a crucial role in shaping the outputs of language models (LMs) to desired styles. In this work, we propose a simple yet effective method, Instruction Model…
Bayesian Reward Models for LLM Alignment
Adam X. Yang, Maxime Robeyns, Thomas Coste +4
To ensure that large language model (LLM) responses are helpful and non-toxic, a reward model trained on human preference data is usually used. LLM responses with high rewards are…
Bayesian Low-rank Adaptation for Large Language Models
Adam X. Yang, Maxime Robeyns, Xi Wang +1
Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs). However, fine-tuned LLMs often become overconfident especia…
How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?
Hannah Kerner, Catherine Nakalembe, Adam Yang +4
Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa,…
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning
Adam X. Yang, Laurence Aitchison, Henry B. Moss
In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating suc…
A theory of representation learning gives a deep generalisation of kernel methods
Adam X. Yang, Maxime Robeyns, Edward Milsom +3
The successes of modern deep machine learning methods are founded on their ability to transform inputs across multiple layers to build good high-level representations. It is theref…
Deep kernel processes
Laurence Aitchison, Adam X. Yang, Sebastian W. Ober
We define deep kernel processes in which positive definite Gram matrices are progressively transformed by nonlinear kernel functions and by sampling from (inverse) Wishart distribu…