9 citations · 21 across the 8 of their papers we have counts for
8 papers
LLM Flow Processes for Text-Conditioned Regression
Felix Biggs, Samuel Willis
Recent work has demonstrated surprisingly good performance of pre-trained LLMs on regression tasks (for example, time-series prediction), with the ability to incorporate expert pri…
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
Felix Biggs, Antonin Schrab, Arthur Gretton
We propose novel statistics which maximise the power of a two-sample test based on the Maximum Mean Discrepancy (MMD), by adapting over the set of kernels used in defining it. For…
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty
Felix Biggs, Benjamin Guedj
We introduce a modified version of the excess risk, which can be used to obtain tighter, fast-rate PAC-Bayesian generalisation bounds. This modified excess risk leverages informati…
A Note on the Efficient Evaluation of PAC-Bayes Bounds
Felix Biggs
When utilising PAC-Bayes theory for risk certification, it is usually necessary to estimate and bound the Gibbs risk of the PAC-Bayes posterior. Many works in the literature employ…
On Margins and Generalisation for Voting Classifiers
Felix Biggs, Valentina Zantedeschi, Benjamin Guedj
We study the generalisation properties of majority voting on finite ensembles of classifiers, proving margin-based generalisation bounds via the PAC-Bayes theory. These provide sta…
Non-Vacuous Generalisation Bounds for Shallow Neural Networks
Felix Biggs, Benjamin Guedj
We focus on a specific class of shallow neural networks with a single hidden layer, namely those with -normalised data and either a sigmoid-shaped Gaussian error function ("er…
On Margins and Derandomisation in PAC-Bayes
Felix Biggs, Benjamin Guedj
We give a general recipe for derandomising PAC-Bayesian bounds using margins, with the critical ingredient being that our randomised predictions concentrate around some value. The…
Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks
Felix Biggs, Benjamin Guedj
We make three related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC-Bayesian setting: (1) we show how averaging over an ens…