36 citations · 122 across the 11 of their papers we have counts for
11 papers
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…
Neuro-Symbolic Hierarchical Rule Induction
Claire Glanois, Xuening Feng, Zhaohui Jiang +4
We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in…
New physics in semileptonic transitions: rare hyperon vs. kaon decays
Li-Sheng Geng, Jorge Martin Camalich, Rui-Xiang Shi
We investigate the potential of rare hyperon decays to probe the short distance structure in the and transitions. Hyperon decays into neutrinos…
Differentiable Logic Machines
Matthieu Zimmer, Xuening Feng, Claire Glanois +6
The integration of reasoning, learning, and decision-making is key to build more general artificial intelligence systems. As a step in this direction, we propose a novel neural-log…
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
Matthieu Zimmer, Claire Glanois, Umer Siddique +1
We consider the problem of learning fair policies in (deep) cooperative multi-agent reinforcement learning (MARL). We formalize it in a principled way as the problem of optimizing…
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning
Jiancong Huang, Juan Rojas, Matthieu Zimmer +3
Policy optimization in reinforcement learning requires the selection of numerous hyperparameters across different environments. Fixing them incorrectly may negatively impact optimi…
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards
Umer Siddique, Paul Weng, Matthieu Zimmer
As the operations of autonomous systems generally affect simultaneously several users, it is crucial that their designs account for fairness considerations. In contrast to standard…
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation
Yijiong Lin, Jiancong Huang, Matthieu Zimmer +2
Deep reinforcement learning (DRL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. We…
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning
Yijiong Lin, Jiancong Huang, Matthieu Zimmer +3
Deep Reinforcement Learning (RL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. To a…
Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains
Matthieu Zimmer, Paul Weng
In the context of learning deterministic policies in continuous domains, we revisit an approach, which was first proposed in Continuous Actor Critic Learning Automaton (CACLA) and…