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Spectral Learning from a Single Trajectory ...
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Document type :
Communication dans un congrès avec actes
Title :
Spectral Learning from a Single Trajectory under Finite-State Policies
Author(s) :
Balle, Borja [Auteur]
Computing Department [Lancaster]
Maillard, Odalric-Ambrym [Auteur] refId
Sequential Learning [SEQUEL]
Conference title :
International conference on Machine Learning
City :
Sidney
Country :
France
Start date of the conference :
2017-07
Journal title :
Proceedings of the International conference on Machine Learning
Publication date :
2017-07
HAL domain(s) :
Mathématiques [math]/Systèmes dynamiques [math.DS]
Mathématiques [math]/Théorie spectrale [math.SP]
Statistiques [stat]/Machine Learning [stat.ML]
English abstract : [en]
We present spectral methods of moments for learning sequential models from a single trajec-tory, in stark contrast with the classical literature that assumes the availability of multiple i.i.d. trajectories. Our approach ...
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We present spectral methods of moments for learning sequential models from a single trajec-tory, in stark contrast with the classical literature that assumes the availability of multiple i.i.d. trajectories. Our approach leverages an efficient SVD-based learning algorithm for weighted au-tomata and provides the first rigorous analysis for learning many important models using dependent data. We state and analyze the algorithm under three increasingly difficult scenarios: proba-bilistic automata, stochastic weighted automata, and reactive predictive state representations controlled by a finite-state policy. Our proofs include novel tools for studying mixing properties of stochastic weighted automata.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
BANDITS MANCHOTS POUR SIGNAUX NON-STATIONNAIRES ET STRUCTURES
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
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