Publications

Aaron Courville

2019-12-12

ArXiv (preprint)

Interactive Psychometrics for Autism with the Human Dynamic Clamp: Interpersonal Synchrony from Sensory-motor to Socio-cognitive Domains

Florence Baillin

Aline Lefebvre

Amandine Pedoux

Yann Beauxis

Denis-Alexander Engemann

Anna Maruani

Frederique Amsellem

Thomas Bourgeron

Richard Delorme

Guillaume Dumas

2019-12-06

(published)

Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia

Clara A. Moreau

Sebastian Urchs

Pierre Orban

Catherine Schramm

Guillaume Dumas

Aurélie Labbe

Guillaume Huguet

Elise Douard

Pierre-Olivier Quirion

Amy Lin

Leila Kushan

Stephanie Grot

David Luck

Adrianna Mendrek

Stephane Potvin

Emmanuel Stip

Thomas Bourgeron

Alan C. Evans

Carrie E. Bearden

Lune Bellec … (see 1 more)

Sébastien Jacquemont

16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Defic… (see more)it-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. We analyzed resting-state functional magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We used CNV FC-signatures to identify dimensions contributing to complex idiopathic conditions. CNVs had large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions played a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibited worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.

2019-12-06

bioRxiv (preprint)

Applying Knowledge Transfer for Water Body Segmentation in Peru

Jessenia Gonzalez

Debjani Bhowmick

César Beltrán

Kris Sankaran

2019-12-02

ArXiv (preprint)

Approximate information state for partially observed systems

Jayakumar Subramanian

Aditya Mahajan

The standard approach for modeling partially observed systems is to model them as partially observable Markov decision processes (POMDPs) an… (see more)d obtain a dynamic program in terms of a belief state. The belief state formulation works well for planning but is not ideal for online reinforcement learning because the belief state depends on the model and, as such, is not observable when the model is unknown.In this paper, we present an alternative notion of an information state for obtaining a dynamic program in partially observed models. In particular, an information state is a sufficient statistic for the current reward which evolves in a controlled Markov manner. We show that such an information state leads to a dynamic programming decomposition. Then we present a notion of an approximate information state and present an approximate dynamic program based on the approximate information state. Approximate information state is defined in terms of properties that can be estimated using sampled trajectories. Therefore, they provide a constructive method for reinforcement learning in partially observed systems. We present one such construction and show that it performs better than the state of the art for three benchmark models.

2019-12-01

IEEE Conference on Decision and Control (published)

Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with Generative Adversarial Neural Networks

T. Yuan

H. Song

David Hall

Victor Schmidt

Kris Sankaran

Automated curriculum generation for Policy Gradients from Demonstrations

Anirudh Srinivasan

Dzmitry Bahdanau

Maxime Chevalier-Boisvert

2019-12-01

ArXiv (preprint)

Expressiveness and Learning of Hidden Quantum Markov Models

Sandesh M. Adhikary

Siddarth Srinivasan

Geoff Gordon

Byron Boots

Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in th… (see more)e development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressiveness of such models and learning them from data. We tackle these problems by showing that HQMMs are a special subclass of the general class of observable operator models (OOMs) that do not suffer from the \emph{negative probability problem} by design. We also provide a feasible retraction-based learning algorithm for HQMMs using constrained gradient descent on the Stiefel manifold of model parameters. We demonstrate that this approach is faster and scales to larger models than previous learning algorithms.

2019-12-01

ArXiv (preprint)

Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model

Lina M. Tran

Sheena A. Josselyn

Blake Richards

Paul W. Frankland

2019-12-01

Behavioural Brain Research (published)

On generalized surrogate duality in mixed-integer nonlinear programming

Benjamin Müller

Gonzalo Muñoz

Maxime Gasse

Ambros Gleixner

Andrea Lodi

Felipe Serrano

2019-12-01

ArXiv (preprint)

Networked control of coupled subsystems: Spectral decomposition and low-dimensional solutions

Shuang Gao

Aditya Mahajan

In this paper, we investigate optimal networked control of coupled subsystems where the dynamics and the cost couplings depend on an underly… (see more)ing weighted graph. We use the spectral decomposition of the graph adjacency matrix to decompose the overall system into (L+1) systems with decoupled dynamics and cost, where L is the rank of the adjacency matrix. Consequently, the optimal control input at each subsystem can be computed by solving (L+1) decoupled Riccati equations. A salient feature of the result is that the solution complexity depends on the rank of the adjacency matrix rather than the size of the network (i.e., the number of nodes). Therefore, the proposed solution framework provides a scalable method for synthesizing and implementing optimal control laws for large-scale systems.

2019-12-01

IEEE Conference on Decision and Control (published)

Restless bandits with controlled restarts: Indexability and computation of Whittle index

Nima Akbarzadeh

Aditya Mahajan

Motivated by applications in machine repair, queueing, surveillance, and clinic care, we consider a scheduling problem where a decision make… (see more)r can reset m out of n Markov processes at each time. Processes that are reset, restart according to a known probability distribution and processes that are not reset, evolve in a Markovian manner. Due to the high complexity of finding an optimal policy, such scheduling problems are often modeled as restless bandits. We show that the model satisfies a technical condition known as indexability. For indexable restless bandits, the Whittle index policy, which computes a function known as Whittle index for each process and resets the m processes with the lowest index, is known to be a good heuristic. The Whittle index is computed by solving an auxiliary Markov decision problem for each arm. When the optimal policy for this auxiliary problem is threshold based, we use ideas from renewal theory to derive closed form expression for the Whittle index. We present detailed numerical experiments which suggest that Whittle index policy performs close to the optimal policy and performs significantly better than myopic policy, which is a commonly used heuristic.

2019-12-01

IEEE Conference on Decision and Control (published)