19 citations · 35 across the 8 of their papers we have counts for
8 papers
Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Zhongwei Yu, Rasul Tutunov, Alexandre Max Maraval +13
Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often w…
Model-Based and Sample-Efficient AI-Assisted Math Discovery in Sphere Packing
Rasul Tutunov, Alexandre Maraval, Antoine Grosnit +3
Sphere packing, Hilbert's eighteenth problem, asks for the densest arrangement of congruent spheres in n-dimensional Euclidean space. Although relevant to areas such as cryptograph…
Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance
Antoine Grosnit, Alexandre Maraval, Refinath S N +16
Human expertise emerges through iterative cycles of interaction, reflection, and internal model updating, which are central to cognitive theories such as Kolb's experiential learni…
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit +1
Meta-Bayesian optimisation (meta-BO) aims to improve the sample efficiency of Bayesian optimisation by leveraging data from related tasks. While previous methods successfully meta-…
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit +3
Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian P…
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval +9
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By…
Efficient Semi-Implicit Variational Inference
Vincent Moens, Hang Ren, Alexandre Maraval +3
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI). Our method, first, maps SIVI's evidence lower bound (ELBO) to a for…
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov +8
In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedastic…