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Found 5 papers, 1 papers with code
Bayesian Reward Models for LLM Alignment
To ensure that large language model (LLM) responses are helpful and non-toxic, we usually fine-tune a reward model on human preference data.
Bayesian Low-rank Adaptation for Large Language Models
Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs).
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning
In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems.
A theory of representation learning gives a deep generalisation of kernel methods
In particular, we show that Deep Gaussian processes (DGPs) in the Bayesian representation learning limit have exactly multivariate Gaussian posteriors, and the posterior covariances can be obtained by optimizing an interpretable objective combining a log-likelihood to improve performance with a series of KL-divergences which keep the posteriors close to the prior.
Deep kernel processes
We show that the deep inverse Wishart process gives superior performance to DGPs and infinite BNNs on standard fully-connected baselines.
