activity
20092026
most citedLarge-Scale Kernel Methods for Independence Testing

101 citations · 214 across the 17 of their papers we have counts for

collaborators

19 papers

stat.AP2026

Multi-Fidelity Gaussian Processes for Translational Modelling of Clinical Outcomes

Isaac S. Hayden, Alicia D'Souza, Steven Niederer +1

Bridging the gap between animal and human experiments remains a major challenge in translational medicine, particularly in early drug development. Progress is constrained by financ…

stat.ME2023★ 4 cited

Automated calibration of consensus weighted distance-based clustering approaches using sharp

Barbara Bodinier, Dragana Vuckovic, Sabrina Rodrigues +3

In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data sugg…

cs.LG2022★ 2 cited

Delayed Feedback in Generalised Linear Bandits Revisited

Benjamin Howson, Ciara Pike-Burke, Sarah Filippi

The stochastic generalised linear bandit is a well-understood model for sequential decision-making problems, with many algorithms achieving near-optimal regret guarantees under imm…

stat.ME2021★ 8 cited

Variational Bayes for high-dimensional proportional hazards models with applications within gene expression

Michael Komodromos, Eric Aboagye, Marina Evangelou +2

Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often e…

cs.LG2021★ 1 cited

Optimism and Delays in Episodic Reinforcement Learning

Benjamin Howson, Ciara Pike-Burke, Sarah Filippi

There are many algorithms for regret minimisation in episodic reinforcement learning. This problem is well-understood from a theoretical perspective, providing that the sequences o…

stat.ME2021★ 33 cited

Automated calibration for stability selection in penalised regression and graphical models

Barbara Bodinier, Sarah Filippi, Therese Haugdahl Nost +2

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated…