Publications

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.
2022-12-05
IEEE Conference on Decision and Control (publié)
doi.org
Pitfalls of conditional computation for multi-modal learning
Ivaxi Sheth
Mohammad Havaei
S Ebrahimi Kahou
Humans have perfected the art of learning from multiple modalities, through sensory organs. Despite impressive predictive performance on a s… (voir plus)ingle modality, neural networks cannot reach human level accuracy with respect to multiple modalities. This is a particularly challenging task due to variations in the structure of respective modalities. A popular method, Conditional Batch Normalization (CBN), was proposed to learn contextual features to aid a deep learning task. This uses the auxiliary data to improve representational power by learning affine transformation for Convolution Neural Networks. Despite the boost in performance by using CBN layer, our work reveals that the visual features learned by introducing auxiliary data via CBN deteriorates. We perform comprehensive experiments to evaluate the brittleness of a dataset to CBN. We show the sensitivity of CBN to the dataset, suggesting that learning from visual features could often be superior for generalization. We perform exhaustive experiments on natural images for bird classification and histology images for cancer type classification. We observe that the CBN network, learns close to no visual features on the bird classification dataset and partial visual features on the histology dataset. Our experiments reveal that CBN may encourage shortcut learning between the auxiliary data and labels.
2022-12-05
NeurIPS.cc/2022/Workshop/ICBINB (poster)
openreview.net
openreview.net
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
2022-12-05
2022 IEEE 61st Conference on Decision and Control (CDC) (publié)
doi.org
Computational brain dynamics in prosopagnosia
Simon Faghel-Soubeyrand
Anne-Raphaelle Richoz
Delphine Waeber
Jessica Woodhams
Frédéric Gosselin
Roberto Caldara
Ian Charest
2022-12-04
Journal of Vision (publié)
doi.org
GRAND for Rayleigh Fading Channels
Syed Mohsin Abbas
Marwan Jalaleddine
Warren J. 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
2022-12-03
2022 IEEE Globecom Workshops (GC Wkshps) (publié)
doi.org
arxiv.org
Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI
Joshua D. Durso-Finley
Jean-Pierre R. Falet
Brennan Nichyporuk
Douglas Arnold
Tal Arbel
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (voir plus)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties,...).
2022-12-03
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (publié)
doi.org
proceedings.mlr.press
Segmentation-Consistent Probabilistic Lesion Counting
Julien Schroeter
Chelsea Myers-colet
Douglas Arnold
Tal Arbel
Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical ima… (voir plus)ging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach—which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting—is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks.
2022-12-03
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (publié)
doi.org
proceedings.mlr.press
Tackling hypo and hyper sensory processing heterogeneity in autism: From clinical stratification to genetic pathways
Aline Lefebvre
Julian Tillmann
Freddy Cliquet
Frederique Amsellem
Anna Maruani
Claire Leblond
Anita Beggiato
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Daniel Umbricht
Christopher H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marion Leboyer
Tony Charman
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
2022-12-03
Autism Research (publié)
doi.org
Performative Prediction in Time Series: A Case Study
Rupali Bhati
Jennifer Jones
David Langelier
Anthony Reiman
Jonathan Greenland
Kristin Campbell
Audrey Durand
2022-12-01
NeurIPS.cc/2022/Workshop/TS4H (poster)
openreview.net
openreview.net
Advancing ethics review practices in AI research
Madhulika Srikumar
Rebecca Finlay
Grace M. Abuhamad
Carolyn Ashurst
Rosie Campbell
Emily Campbell-Ratcliffe
Hudson Hongo
Sara Rene Jordan
Joseph Lindley
Aviv Ovadya
Joelle Pineau
2022-11-30
Nature Machine Intelligence (publié)
doi.org
APOE alleles are associated with sex-specific structural differences in brain regions affected in Alzheimer's disease and related dementia
Chloé Savignac
Sylvia Villeneuve
AmanPreet Badhwar
Karin Saltoun
Kimia Shafighi
Chris Zajner
Vaibhav Sharma
Sarah A. Gagliano Taliun
Sali Farhan
Judes Poirier
Danilo Bzdok
Alzheimer’s disease is marked by intracellular tau aggregates in the medial temporal lobe (MTL) and extracellular amyloid aggregates in th… (voir plus)e default network (DN). Here, we examined codependent structural variations between the MTL’s most vulnerable structure, the hippocampus (HC), and the DN at subregion resolution in individuals with Alzheimer’s disease and related dementia (ADRD). By leveraging the power of the approximately 40,000 participants of the UK Biobank cohort, we assessed impacts from the protective APOE ɛ2 and the deleterious APOE ɛ4 Alzheimer’s disease alleles on these structural relationships. We demonstrate ɛ2 and ɛ4 genotype effects on the inter-individual expression of HC-DN co-variation structural patterns at the population level. Across these HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix’s fimbria, and their cortical partners related to ADRD risk. Analyses of the rich phenotypic profiles in the UK Biobank cohort further revealed male-specific HC-DN associations with air pollution and female-specific associations with cardiovascular traits. We also showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our structural, genetic, and phenotypic analyses in this large epidemiological cohort reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex and link APOE alleles to inter-individual brain structural differences indicative of ADRD familial risk.
2022-11-30
PLoS Biology (publié)
doi.org
Autism incidence and spatial analysis in more than 7 million pupils in English schools: a retrospective, longitudinal, school registry study.
Andres Roman-Urrestarazu
Justin Christopher Yang
R. van Kessel
Varun Warrier
Guillaume Dumas
H. Jongsma
Gabriel Gatica-bahamonde
Carrie Allison
F. Matthews
Simon Baron-Cohen
Carol Brayne
2022-11-30
The Lancet Child & Adolescent Health (publié)
doi.org