Publications

Detection and genomic analysis of BRAF fusions in Juvenile Pilocytic Astrocytoma through the combination and integration of multi-omic data
Melissa Zwaig
Audrey Baguette
Bo Hu
Michael Johnston
Hussein Lakkis
Emily M. Nakada
Damien Faury
Nikoleta Juretic
Benjamin Ellezam
Alexandre G. Weil
Jason Karamchandani
Jacek Majewski
Mathieu Blanchette
Michael D. Taylor
Marco Gallo
Claudia Kleinman
Nada Jabado
Jiannis Ragoussis
2022-12-12
BMC Cancer (publié)
doi.org
Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
Alexandre Larouche
Audrey Durand
Richard Khoury
Caroline Sirois
2022-12-10
ArXiv (prépublication)
doi.org
arxiv.org
Energy efficiency as a normative account for predictive coding
Shahab Bakhtiari
2022-12-09
Patterns (publié)
doi.org
Implicit Offline Reinforcement Learning via Supervised Learning
Alexandre Piché
Rafael Pardinas
David Vazquez
Igor Mordatch
Chris Pal
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset of varied b… (voir plus)ehaviors. It is as simple as supervised learning and Behavior Cloning (BC) but takes advantage of the return information. On BC tasks, implicit models have been shown to match or outperform explicit ones. Despite the benefits of using implicit models to learn robotic skills via BC, Offline RL via Supervised Learning algorithms have been limited to explicit models. We show how implicit models leverage return information and match or outperform explicit algorithms to acquire robotic skills from fixed datasets. Furthermore, we show how closely related our implicit methods are to other popular RL via Supervised Learning algorithms.
2022-12-09
NeurIPS.cc/2022/Workshop/DeepRL (inconnu)
doi.org
openreview.net
Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing
Rehab Alhasani
Mathieu Godbout
Audrey Durand
Claudine Auger
Anouk Lamontagne
Sara Ahmed
2022-12-09
BMC Neurology (publié)
doi.org
PyNM: a Lightweight Python implementation of Normative Modeling
Annabelle Harvey
Guillaume Dumas
The majority of studies in neuroimaging and psychiatry are focussed on case-control analysis (Marquand et al., 2019). However, case-control … (voir plus)relies on well-defined groups which is more the exception than the rule in biology. Psychiatric conditions are diagnosed based on symptoms alone, which makes for heterogeneity at the biological level (Marquand et al., 2016). Relying on mean differences obscures this heterogeneity and the resulting loss of information can produce unreliable results or misleading conclusions (Loth et al., 2021).
2022-12-08
Journal of Open Source Software (publié)
doi.org
Consistency and Rate of Convergence of Switched Least Squares System Identification for Autonomous Markov Jump Linear Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In this paper, we investigate the problem of system identification for autonomous Markov jump linear systems (MJS) with complete state obser… (voir plus)vations. We propose switched least squares method for identification of MJS, show that this method is strongly consistent, and derive data-dependent and data-independent rates of convergence. In particular, our data-dependent rate of convergence shows that, almost surely, the system identification error is
2022-12-06
IEEE Conference on Decision and Control (publié)
doi.org
A modified Thompson sampling-based learning algorithm for unknown linear systems
Mukul Gagrani
Sagar Sudhakara
Aditya Mahajan
Rahul Jain
Ashutosh Nayyar
Yi Ouyang
We revisit the Thompson sampling-based learning algorithm for controlling an unknown linear system with quadratic cost proposed in [1]. This… (voir plus) algorithm operates in episodes of dynamic length and it is shown to have a regret bound of
2022-12-06
2022 IEEE 61st Conference on Decision and Control (CDC) (publié)
doi.org
arxiv.org
Partially observable restless bandits with restarts: indexability and computation of Whittle index
Nima Akbarzadeh
Aditya Mahajan
We consider restless bandits with restarts, where the state of the active arms resets according to a known probability distribution while th… (voir plus)e state of the passive arms evolves in a Markovian manner. We assume that the state of the arm is observed after it is reset but not observed otherwise. We show that the model is indexable and propose an efficient algorithm to compute the Whittle index by exploiting the qualitative properties of the optimal policy. A detailed numerical study of machine repair models shows that Whittle index policy outperforms myopic policy and is close to optimal policy.
2022-12-06
IEEE Conference on Decision and Control (publié)
doi.org
Thompson-Sampling Based Reinforcement Learning for Networked Control of Unknown Linear Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In recent years, there has been considerable interest in reinforcement learning for linear quadratic Gaussian (LQG) systems. In this paper, … (voir plus)we consider a generalization of such systems where the controller and the plant are connected over an unreliable packet drop channel. Packet drops cause the system dynamics to switch between controlled and uncontrolled modes. This switching phenomena introduces new challenges in designing learning algorithms. We identify a sufficient condition under which the regret of Thompson sampling-based reinforcement learning algorithm with dynamic episodes (TSDE) at horizon T is bounded by
2022-12-06
2022 IEEE 61st Conference on Decision and Control (CDC) (publié)
doi.org
Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning
Mariangela Panniello
Colleen J Gillon
Roberto Maffulli
Marco Celotto
Stefano Panzeri
Blake Richards
Michael M Kohl
2022-12-05
bioRxiv (prépublication)
doi.org
GRAND for Rayleigh Fading Channels
Syed Mohsin Abbas
Marwan Jalaleddine
Warren Gross
Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding technique for short-length and high-rate channel codes. GRAND at… (voir plus)tempts to guess the channel-induced noise by generating Test Error Patterns (TEPs), and the sequence of TEP generation is the primary distinction between GRAND variants. In this work, we extend the application of GRAND to multipath frequency non-selective Rayleigh fading communication channels, and we refer to this GRAND variant as Fading-GRAND. The proposed Fading-GRAND adapts its TEP generation to the fading conditions of the underlying communication channel, outperforming traditional channel code decoders in scenarios with L spatial diversity branches as well as scenarios with no diversity. Numerical simulation results show that the Fading-GRAND outperforms the traditional Berlekamp-Massey (B-M) decoder for decoding BCH code (127, 106) and BCH code (127, 113) by
2022-12-04
2022 IEEE Globecom Workshops (GC Wkshps) (publié)
doi.org
arxiv.org