Portrait de Prakash Panangaden

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

Optimal Approximate Minimization of One-Letter Weighted Finite Automata
Clara Lacroce
Borja Balle
Prakash Panangaden
Guillaume Rabusseau
2024-11-08
Mathematical Structures in Computer Science (publié)
doi.org
arxiv.org
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies
Jonathan Colaco Carr
Prakash Panangaden
Doina Precup
2024-04-18
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
arxiv.org
Polynomial Lawvere Logic
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon D. Plotkin
2024-02-05
ArXiv (prépublication)
doi.org
arxiv.org
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Prakash Panangaden
Sahand Rezaei-Shoshtari
Rosie Zhao
David Meger
Doina Precup
arxiv.org
Sum and Tensor of Quantitative Effects
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon Plotkin
2024-01-01
Log. Methods Comput. Sci. (publié)
doi.org
arxiv.org
Behavioural pseudometrics for continuous-time diffusions
Linan Chen
Florence Clerc
Prakash Panangaden
2023-12-27
ArXiv (prépublication)
doi.org
arxiv.org
Propositional Logics for the Lawvere Quantale
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon Plotkin
2023-11-23
Electronic Notes in Theoretical Informatics and Computer Science (publié)
doi.org
arxiv.org
Behavioural equivalences for continuous-time Markov processes
Linan Chen
Florence Clerc
Prakash Panangaden
2023-03-30
Mathematical Structures in Computer Science (publié)
doi.org
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes
Pablo Samuel Castro
Tyler Kastner
Prakash Panangaden
Mark Rowland
We present a novel perspective on behavioural metrics for Markov decision processes via the use of positive definite kernels. We define a ne… (voir plus)w metric under this lens that is provably equivalent to the recently introduced MICo distance (Castro et al., 2021). The kernel perspective enables us to provide new theoretical results, including value-function bounds and low-distortion finite-dimensional Euclidean embeddings, which are crucial when using behavioural metrics for reinforcement learning representations. We complement our theory with strong empirical results that demonstrate the effectiveness of these methods in practice.
2023-01-01
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Augmenting Human Selves Through Artificial Agents – Lessons From the Brain
Georg Northoff
Maia Fraser
John Griffiths
Dimitris A. Pinotsis
Prakash Panangaden
Rosalyn Moran
Karl Friston
Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for f… (voir plus)unctional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.
2022-06-23
Frontiers in Computational Neuroscience (publié)
doi.org
Towards an AAK Theory Approach to Approximate Minimization in the Multi-Letter Case
Clara Lacroce
Prakash Panangaden
Guillaume Rabusseau
We study the approximate minimization problem of weighted finite automata (WFAs): given a WFA, we want to compute its optimal approximation … (voir plus)when restricted to a given size. We reformulate the problem as a rank-minimization task in the spectral norm, and propose a framework to apply Adamyan-Arov-Krein (AAK) theory to the approximation problem. This approach has already been successfully applied to the case of WFAs and language modelling black boxes over one-letter alphabets \citep{AAK-WFA,AAK-RNN}. Extending the result to multi-letter alphabets requires solving the following two steps. First, we need to reformulate the approximation problem in terms of noncommutative Hankel operators and noncommutative functions, in order to apply results from multivariable operator theory. Secondly, to obtain the optimal approximation we need a version of noncommutative AAK theory that is constructive. In this paper, we successfully tackle the first step, while the second challenge remains open.
2022-06-01
ArXiv (prépublication)
doi.org
arxiv.org
Bisimulation metrics and norms for real-weighted automata
Borja Balle
Pascale Gourdeau
Prakash Panangaden
2022-01-01
Information and Computation (publié)
doi.org