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2020 – today
- 2021
[j2]François Torregrossa
, Robin Allesiardo, Vincent Claveau, Nihel Kooli, Guillaume Gravier:
A survey on training and evaluation of word embeddings. Int. J. Data Sci. Anal. 11(2): 85-103 (2021)- [c12]François Torregrossa, Robin Allesiardo, Vincent Claveau, Guillaume Gravier:
Unsupervised Tree Extraction in Embedding Spaces for Taxonomy Induction. WI/IAT 2021: 302-309 - 2020
- [c11]Cheikh Brahim El Vaigh, François Torregrossa, Robin Allesiardo, Guillaume Gravier, Pascale Sébillot:
A correlation-based entity embedding approach for robust entity linking. ICTAI 2020: 949-954 - [c10]François Torregrossa, Vincent Claveau, Nihel Kooli, Guillaume Gravier, Robin Allesiardo:
On the Correlation of Word Embedding Evaluation Metrics. LREC 2020: 4789-4797
2010 – 2019
- 2019
- [c9]Robin Allesiardo, Christophe Sauldubois, Fabrice Depaulis, Nicolas Bulteau, Frédéric Chantrel, Erwan Pigneul:
A Practical Approach to Intelligent Spoken Dialogue for Third-Party Applications on Home Devices with Linear Bandits. EGC (best of volume) 2019: 65-76 - [c8]Robin Allesiardo, Christophe Sauldubois, Fabrice Depaulis, Nicolas Bulteau, Frédéric Chantrel, Erwan Pigneul:
Une méthodologie pour l'implémentation d'applications tierces intelligentes à destination des assistants vocaux via des bandits linéaires. EGC 2019: 243-248 - [c7]François Torregrossa, Nihel Kooli, Robin Allesiardo, Erwan Pigneul:
How We Achieved a Production Ready Slot Filling Deep Neural Network Without Initial Natural Language Data. ICONIP (4) 2019: 247-255 - 2018
- [c6]Nihel Kooli, Robin Allesiardo, Erwan Pigneul:
Deep Learning Based Approach for Entity Resolution in Databases. ACIIDS (2) 2018: 3-12 - 2017
- [j1]Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard:
The non-stationary stochastic multi-armed bandit problem. Int. J. Data Sci. Anal. 3(4): 267-283 (2017) - [c5]Robin Allesiardo, Raphaël Féraud:
Selection of learning experts. IJCNN 2017: 1005-1010 - 2016
- [b1]Robin Allesiardo:
Bandits Manchots sur Flux de Données Non Stationnaires. (Multi-armed bandits for non-stationary data streams). University of Paris-Saclay, France, 2016 - [c4]Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot:
Random Forest for the Contextual Bandit Problem. AISTATS 2016: 93-101 - [i3]Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard:
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem. CoRR abs/1609.02139 (2016) - 2015
- [c3]Robin Allesiardo, Raphaël Féraud:
EXP3 with drift detection for the switching bandit problem. DSAA 2015: 1-7 - [i2]Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot:
Decision Tree Algorithms for the Contextual Bandit Problem. CoRR abs/1504.06952 (2015) - 2014
- [c2]Robin Allesiardo, Raphaël Féraud, Djallel Bouneffouf:
A Neural Networks Committee for the Contextual Bandit Problem. ICONIP (1) 2014: 374-381 - [c1]Djallel Bouneffouf, Romain Laroche, Tanguy Urvoy, Raphaël Féraud, Robin Allesiardo:
Contextual Bandit for Active Learning: Active Thompson Sampling. ICONIP (1) 2014: 405-412 - [i1]Robin Allesiardo, Raphaël Féraud, Djallel Bouneffouf:
A Neural Networks Committee for the Contextual Bandit Problem. CoRR abs/1409.8191 (2014)
Coauthor Index
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