106 citations · 173 across the 10 of their papers we have counts for
10 papers
Controllable Neural Symbolic Regression
Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny
In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variable…
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
F. O. de Franca, M. Virgolin, M. Kommenda +21
Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model tha…
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier +1
Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data. Recently, deep neural models trained on procedurally-generated synthetic datasets sho…
End-to-end symbolic regression with transformers
Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample +1
Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step proc…
Deep Symbolic Regression for Recurrent Sequences
Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample +1
Symbolic regression, i.e. predicting a function from the observation of its values, is well-known to be a challenging task. In this paper, we train Transformers to infer the functi…
SaLinA: Sequential Learning of Agents
Ludovic Denoyer, Alfredo de la Fuente, Song Duong +3
SaLinA is a simple library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms. It is built as an extension of PyTorch: alg…
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
Pierre-Alexandre Kamienny, Jean Tarbouriech, Sylvain Lamprier +2
Learning meaningful behaviors in the absence of reward is a difficult problem in reinforcement learning. A desirable and challenging unsupervised objective is to learn a set of div…
Privileged Information Dropout in Reinforcement Learning
Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal Behbahani +2
Using privileged information during training can improve the sample efficiency and performance of machine learning systems. This paradigm has been applied to reinforcement learning…
Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization
Pierre-Alexandre Kamienny, Matteo Pirotta, Alessandro Lazaric +3
We study the problem of learning exploration-exploitation strategies that effectively adapt to dynamic environments, where the task may change over time. While RNN-based policies c…
FACMAC: Factored Multi-Agent Centralised Policy Gradients
Bei Peng, Tabish Rashid, Christian A. Schroeder de Witt +4
We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces…