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Vincent François-Lavet
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2020 – today
- 2024
- [j7]Floris den Hengst
, Martijn Otten
Guideline-informed reinforcement learning for mechanical ventilation in critical care. Artif. Intell. Medicine 147: 102742 (2024) - [j6]Ameet R. Jagesar
Comparative performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data. Int. J. Medical Informatics 188: 105477 (2024) - [j5]Amjad Yousef Majid
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey. IEEE Trans. Neural Networks Learn. Syst. 35(9): 11939-11957 (2024) - [i20]Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet:
Latent Assistance Networks: Rediscovering Hyperbolic Tangents in RL. CoRR abs/2406.09079 (2024) - [i19]Taewoon Kim, Vincent François-Lavet, Michael Cochez:
Leveraging Knowledge Graph-Based Human-Like Memory Systems to Solve Partially Observable Markov Decision Processes. CoRR abs/2408.05861 (2024) - 2023
- [c17]Taewoon Kim
A Machine with Short-Term, Episodic, and Semantic Memory Systems. AAAI 2023: 48-56 - [c16]Andreas W. M. Sauter, Erman Acar, Vincent François-Lavet:
A Meta-Reinforcement Learning Algorithm for Causal Discovery. CLeaR 2023: 602-619 - [c15]Olivier Moulin, Vincent François-Lavet, Paul W. G. Elbers, Mark Hoogendoorn:
Improving Generalization in Reinforcement Learning Through Forked Agents. IEA/AIE (2) 2023: 249-260 - [c14]Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet:
Disentangled (Un)Controllable Features. SSCI 2023: 695-702 - 2022
- [j4]Floris den Hengst
Planning for potential: efficient safe reinforcement learning. Mach. Learn. 111(6): 2255-2274 (2022) - [c13]Floris den Hengst
Reinforcement Learning with Option Machines. IJCAI 2022: 2909-2915 - [c12]Olivier Moulin, Vincent François-Lavet, Paul W. G. Elbers, Mark Hoogendoorn:
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents. WI/IAT 2022: 166-173 - [i18]Taewoon Kim, Michael Cochez, Vincent François-Lavet, Mark A. Neerincx, Piek Vossen:
A Machine With Human-Like Memory Systems. CoRR abs/2204.01611 (2022) - [i17]Olivier Moulin, Vincent François-Lavet, Paul W. G. Elbers, Mark Hoogendoorn:
Improving adaptability to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents. CoRR abs/2204.06550 (2022) - [i16]Andreas Sauter, Erman Acar
A Meta-Reinforcement Learning Algorithm for Causal Discovery. CoRR abs/2207.08457 (2022) - [i15]Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet:
Disentangled (Un)Controllable Features. CoRR abs/2211.00086 (2022) - [i14]Taewoon Kim, Michael Cochez, Vincent François-Lavet, Mark A. Neerincx, Piek Vossen:
A Machine with Short-Term, Episodic, and Semantic Memory Systems. CoRR abs/2212.02098 (2022) - [i13]Olivier Moulin, Vincent François-Lavet, Mark Hoogendoorn:
Improving generalization in reinforcement learning through forked agents. CoRR abs/2212.06451 (2022) - 2021
- [c11]Shenyang Huang, Vincent François-Lavet, Guillaume Rabusseau:
Understanding Capacity Saturation in Incremental Learning. Canadian AI 2021 - [i12]Bonnie Li, Vincent François-Lavet, Thang Doan, Joelle Pineau:
Domain Adversarial Reinforcement Learning. CoRR abs/2102.07097 (2021) - [i11]Amjad Yousef Majid, Serge Saaybi, Tomas van Rietbergen, Vincent François-Lavet, R. Venkatesha Prasad, Chris J. M. Verhoeven:
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey. CoRR abs/2110.01411 (2021) - [i10]Geoffrey van Driessel, Vincent François-Lavet:
Component Transfer Learning for Deep RL Based on Abstract Representations. CoRR abs/2111.11525 (2021) - 2020
- [c10]Maxime Wabartha, Audrey Durand, Vincent François-Lavet, Joelle Pineau:
Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks. IJCAI 2020: 2140-2147 - [c9]Vincent François-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau:
On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract). IJCAI 2020: 5055-5059 - [c8]Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau:
Novelty Search in Representational Space for Sample Efficient Exploration. NeurIPS 2020 - [i9]Stefano Alletto, Shenyang Huang, Vincent François-Lavet, Yohei Nakata, Guillaume Rabusseau:
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning. CoRR abs/2003.01181 (2020) - [i8]Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau:
Novelty Search in representational space for sample efficient exploration. CoRR abs/2009.13579 (2020)
2010 – 2019
- 2019
- [j3]Vincent François-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau:
On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability. J. Artif. Intell. Res. 65: 1-30 (2019) - [c7]Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau:
Combined Reinforcement Learning via Abstract Representations. AAAI 2019: 3582-3589 - [i7]Shenyang Huang, Vincent François-Lavet, Guillaume Rabusseau:
Neural Architecture Search for Class-incremental Learning. CoRR abs/1909.06686 (2019) - 2018
- [j2]Vincent François-Lavet, Peter Henderson, Riashat Islam
An Introduction to Deep Reinforcement Learning. Found. Trends Mach. Learn. 11(3-4): 219-354 (2018) - [c6]Joshua Romoff, Peter Henderson, Alexandre Piché, Vincent François-Lavet, Joelle Pineau:
Reward Estimation for Variance Reduction in Deep Reinforcement Learning. CoRL 2018: 674-699 - [c5]Joshua Romoff, Alexandre Piché, Peter Henderson, Vincent François-Lavet, Joelle Pineau:
Reward Estimation for Variance Reduction in Deep Reinforcement Learning. ICLR (Workshop) 2018 - [i6]Joshua Romoff, Alexandre Piché, Peter Henderson, Vincent François-Lavet, Joelle Pineau:
Reward Estimation for Variance Reduction in Deep Reinforcement Learning. CoRR abs/1805.03359 (2018) - [i5]Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau:
Combined Reinforcement Learning via Abstract Representations. CoRR abs/1809.04506 (2018) - [i4]Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau:
An Introduction to Deep Reinforcement Learning. CoRR abs/1811.12560 (2018) - 2017
- [b1]Vincent François-Lavet:
Contributions to deep reinforcement learning and its applications in smartgrids. University of Liège, Belgium, 2017 - [c4]Michael Castronovo, Vincent François-Lavet, Raphaël Fonteneau, Damien Ernst, Adrien Couëtoux:
Approximate Bayes Optimal Policy Search using Neural Networks. ICAART (2) 2017: 142-153 - [i3]Vincent François-Lavet, Damien Ernst, Raphael Fonteneau:
On overfitting and asymptotic bias in batch reinforcement learning with partial observability. CoRR abs/1709.07796 (2017) - 2015
- [c3]Samy Aittahar, Vincent François-Lavet, Stefan Lodeweyckx, Damien Ernst, Raphaël Fonteneau:
Imitative Learning for Online Planning in Microgrids. DARE 2015: 1-15 - [i2]Vincent François-Lavet, Raphaël Fonteneau, Damien Ernst:
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies. CoRR abs/1512.02011 (2015) - 2014
- [c2]Vincent François-Lavet, Raphaël Fonteneau, Damien Ernst:
Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device. ADPRL 2014: 1-8 - [c1]Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts:
Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging. Neural Connectomics 2014: 23-34 - [i1]Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts:
Simple connectome inference from partial correlation statistics in calcium imaging. CoRR abs/1406.7865 (2014) - 2013
- [j1]Vincent François-Lavet, François Henrotte, Laurent Stainier
An energy-based variational model of ferromagnetic hysteresis for finite element computations. J. Comput. Appl. Math. 246: 243-250 (2013)
Coauthor Index
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