Prakash Panangaden

Core Academic Member

prakash.panangaden@mila.quebec

Research Topics

Machine Learning Theory

Probabilistic Models

Quantum Information Theory

Reasoning

Reinforcement Learning

Google Scholar

Biography

Prakash Panangaden studied physics at IIT Kanpur, India. He obtained an MS in physics at the University of Chicago studying stimulated emission from blacks holes. He obtained a PhD in physics from the University of Wisconsin-Milwaukee working on quantum field theory in curved spacetime. He was an assistant professor of computer science at Cornell University where he primarily worked on semantics of concurrent programming languages.

Since 1990 he was a professor at McGill University's School of Computer Science and for the last 25 years he has been working on many aspects of Markov processes: process equivalence, logical characterization, approximation and metrics. Recently he has worked on using metrics to enhance representation learning. He has also published papers in physics, quantum information and pure mathematics. Prakash is a Core Academic Member at Mila - Quebec Institute of Artificial Intelligence. He is a Fellow of the Royal Society of Canada and a Fellow of the Association for Computing Machinery (ACM).

Current Students

Jonathan Colaço Carr

Master's Research - McGill University

Co-supervisor :

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 (published)

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 (preprint)

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… (see more)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… (see more)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 (preprint)

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… (see more)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