35 citations · 243 across the 61 of their papers we have counts for
6 papers · 1 filter
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer +13
A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly e…
Why Can Large Language Models Generate Correct Chain-of-Thoughts?
Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek +2
This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting. We investig…
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization
Kamil Dreczkowski, Antoine Grosnit, Haitham Bou Ammar
This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic benchmarking and standardized evaluati…
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
Piotr Kicki, Puze Liu, Davide Tateo +4
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion…
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Juliusz Ziomek, Haitham Bou-Ammar
Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems. However, the success of these techni…
Contextual Causal Bayesian Optimisation
Vahan Arsenyan, Antoine Grosnit, Haitham Bou-Ammar +1
We introduce a unified framework for contextual and causal Bayesian optimisation, which aims to design intervention policies maximising the expectation of a target variable. Our ap…