Publications

Optimism and Adaptivity in Policy Optimization
Veronica Chelu
Tom Zahavy
Arthur Guez
Doina Precup
Sebastian Flennerhag
2022-12-31
arXiv.org (preprint)
doi.org
Optimizing Fairness over Time with Homogeneous Workers (Short Paper).
Bart-jan Van Rossum
Ying Chen
Andrea Lodi
2022-12-31
ATMOS (published)
doi.org
Patient experience or patient satisfaction? A systematic review of child- and family-reported experience measures in pediatric surgery.
Julia Ferreira
Prachikumari Patel
Elena Guadagno
Nikki Ow
Jo Wray
Sherif Emil
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (published)
doi.org
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation
Houssem Ben Braiek
Thomas Reid
Foutse Khomh
In the context of aircraft system performance assessment, deep learning technologies allow us to quickly infer models from experimental meas… (see more)urements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This article presents a novel approach, physics-guided adversarial machine learning (ML), which improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with a physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both the models and in improving their propensity to be consistent with physics domain knowledge.
2022-12-31
IEEE Transactions on Reliability (published)
doi.org
arxiv.org
PipeBERT: High-throughput BERT Inference for ARM Big.LITTLE Multi-core Processors.
Hung-Yang Chang
Seyyed Hasan Mozafari
Cheng Chen
James J. Clark
Brett H. Meyer
Warren J. Gross
2022-12-31
J. Signal Process. Syst. (published)
doi.org
Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study
Jonathan Jalbert
Jean-Noel Weller
Pierre-Luc Boivin
Sylvain Lavigne
Mehdi Taobane
Mike Pieper
Andrea Lodi
Heloise Cardinal
By providing personalized quantitative estimates of time to and quality of future offers, our new approach can inform the shared decision-ma… (see more)king process between transplant candidates and physicians when a kidney offer from a deceased donor is made by an ODO.
2022-12-31
Canadian Journal of Kidney Health and Disease (published)
doi.org
Questions Are All You Need to Train a Dense Passage Retriever
Devendra Singh Sachan
Mike Lewis
Dani Yogatama
Luke Zettlemoyer
Joelle Pineau
Manzil Zaheer
We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training da… (see more)ta. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples. ART, in contrast, only requires access to unpaired inputs and outputs (e.g. questions and potential answer documents). It uses a new document-retrieval autoencoding scheme, where (1) an input question is used to retrieve a set of evidence documents, and (2) the documents are then used to compute the probability of reconstructing the original question. Training for retrieval based on question reconstruction enables effective unsupervised learning of both document and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning. Extensive experiments demonstrate that ART obtains state-of-the-art results on multiple QA retrieval benchmarks with only generic initialization from a pre-trained language model, removing the need for labeled data and task-specific losses.
2022-12-31
Transactions of the Association for Computational Linguistics (published)
doi.org
arxiv.org
A rapid review for developing a co-design framework for a pediatric surgical communication application
Michelle Cwintal
Hamed Ranjbar
Parsa Bandamiri
Elena Guadagno
Esli Osmanlliu
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (published)
doi.org
Real-Time Simultaneous Localization and Mapping with LiDAR intensity
Wenqiang Du
Giovanni Beltrame
We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method , which addresses the geometry degener… (see more)acy problem in unstructured environments. Traditional LiDAR-based front-end odometry mostly relies on geometric features such as points, lines and planes. A lack of these features in the environment can lead to the failure of the entire odometry system. To avoid this problem, we extract feature points from the LiDAR-generated point cloud that match features identified in LiDAR intensity images. We then use the extracted feature points to perform scan registration and estimate the robot ego-movement. For the back-end, we jointly optimize the distance between the corresponding feature points, and the point to plane distance for planes identified in the map. In addition, we use the features extracted from intensity images to detect loop closure candidates from previous scans and perform pose graph optimization. Our experiments show that our method can run in real time with high accuracy and works well with illumination changes, low-texture, and unstructured environments.
2022-12-31
IEEE International Conference on Robotics and Automation (published)
doi.org
arxiv.org
Recall as a Measure of Ranking Robustness
Fernando Diaz
Bhaskar Mitra
2022-12-31
arXiv.org (preprint)
doi.org
Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study
Jianzhong Chen
Leon Qi Rong Ooi
Trevor Wei Kiat Tan
Shaoshi Zhang
Jingwei Li
Christopher L. Asplund
Simon B. Eickhoff
Danilo Bzdok
Avram J. Holmes
B.T. Thomas Yeo
There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation… (see more) of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.
2022-12-31
NeuroImage (published)
doi.org
A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn
Hao-Ting Wang
Steven L. Meisler
Hanad Sharmarke
Natasha Clarke
Nicolas Gensollen
Christopher J Markiewicz
Fraçois Paugam
Bertrand Thirion
Lune P Bellec
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) analyses. Many viable str… (see more)ategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a fully reproducible denoising benchmark featuring a range of denoising strategies and evaluation metrics, built primarily on the fMRIPrep and Nilearn software packages. We apply this reproducible benchmark to investigate the robustness of the conclusions across two different datasets and two versions of fMRIPrep. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks, especially ICA-AROMA. These results demonstrate that a reproducible denoising benchmark can effectively assess the robustness of conclusions across multiple datasets and software versions. Technologies such as BIDS-App, the Jupyter Book and Neurolibre provided the infrastructure to publish the metadata and report figures. Readers can reproduce the report figures beyond the ones reported in the published manuscript. With the denoising benchmark, we hope to provide useful guidelines for the community, and that our software infrastructure will facilitate continued development as the state-of-the-art advances.
2022-12-31
(published)
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