Prakash Panangaden

Membre académique principal

prakash.panangaden@mila.quebec

Sujets de recherche

Apprentissage par renforcement

Modèles probabilistes

Raisonnement

Théorie de l'apprentissage automatique

Théorie de l'information quantique

Google Scholar

Biographie

Prakash Panangaden a étudié la physique à l'Indian Institute of Technology de Kanpur, en Inde. Il a obtenu une maîtrise en physique de l'Université de Chicago, où il a étudié l'émission stimulée des trous noirs. Il a ensuite obtenu un doctorat en physique de l'Université du Wisconsin-Milwaukee, dans lequel il s’est penché sur la théorie quantique des champs dans un espace-temps courbe.

Il a été professeur adjoint d'informatique à l'Université Cornell, où il a principalement travaillé sur la sémantique des langages de programmation concurrents. Depuis 1990, il travaillait à l'Université McGill. Au cours des 25 dernières années, il s'est intéressé à de nombreux aspects des processus de Markov : équivalence des processus, caractérisation logique, approximation et métrique.

Récemment, il a travaillé sur l'utilisation des métriques pour améliorer l'apprentissage des représentations. Il a également publié des articles sur la physique, l'information quantique et les mathématiques pures. Il est membre de la Société royale du Canada et de l'Association for Computing Machinery (ACM).

Étudiants actuels

Jonathan Colaço Carr

Maîtrise recherche - McGill

Co-superviseur⋅e :

Doina Precup

Publications

Extracting Weighted Automata for Approximate Minimization in Language Modelling

Clara Lacroce

Prakash Panangaden

Guillaume Rabusseau

2021-08-25

Proceedings of the Fifteenth International Conference on Grammatical Inference (publié)

proceedings.mlr.press

arxiv.org

Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata

Borja Balle

Clara Lacroce

Prakash Panangaden

Doina Precup

Guillaume Rabusseau

We address the approximate minimization problem for weighted finite automata (WFAs) with weights in …

2021-02-13

ArXiv (prépublication)

doi.org

arxiv.org

MICo: Improved representations via sampling-based state similarity for Markov decision processes

Pablo Samuel Castro

Tyler Kastner

Prakash Panangaden

Mark Rowland

We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effe… (voir plus)ctive means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analyses, we provide empirical evidence that learning this distance alongside the value function yields structured and informative representations, including strong results on the Arcade Learning Environment benchmark.

openreview.net

MICo: Learning improved representations via sampling-based state similarity for Markov decision processes

Pablo Samuel Castro

Tyler Kastner

Prakash Panangaden

Mark Rowland

We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an eﬀ… (voir plus)ective means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically diﬃcult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analysis

2021-01-01

arXiv.org (prépublication)

dblp.uni-trier.de

Bisimulation metrics and norms for real-weighted automata

Borja Balle

Pascale Gourdeau

Prakash Panangaden

2020-11-01

Information and Computation (published)

doi.org

A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

Philip. Amortila

Doina Precup

Prakash Panangaden

Marc Gendron-Bellemare

We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate it… (voir plus)s effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods. We show that value-based methods such as TD(

2020-03-27

ArXiv (preprint)

arxiv.org