Publications

Towards Causal Federated Learning For Enhanced Robustness and Privacy
Sreya Francis
Irene Tenison
Irina Rish
Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distribu… (voir plus)ted training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-iid), and Out of Distribution(OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.
2021-04-14
ArXiv (prépublication)
arxiv.org
arxiv.org
Science-Software Linkage: The Challenges of Traceability between Scientific Knowledge and Software Artifacts
Hideaki Hata
Jin Guo
Raula Gaikovina Kula
Christoph Treude
Although computer science papers are often accompanied by software artifacts, connecting research papers to their software artifacts and vic… (voir plus)e versa is not always trivial. First of all, there is a lack of well-accepted standards for how such links should be provided. Furthermore, the provided links, if any, often become outdated: they are affected by link rot when pre-prints are removed, when repositories are migrated, or when papers and repositories evolve independently. In this paper, we summarize the state of the practice of linking research papers and associated source code, highlighting the recent efforts towards creating and maintaining such links. We also report on the results of several empirical studies focusing on the relationship between scientific papers and associated software artifacts, and we outline challenges related to traceability and opportunities for overcoming these challenges.
2021-04-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke
Matthias Eisenmann
Minu Dietlinde Tizabi
Carole H. Sudre
TIM RÄDSCH
Michela Antonelli
Tal Arbel
Spyridon Bakas
M. Jorge Cardoso
Veronika Cheplygina
Keyvan Farahani
B. Glocker
DOREEN HECKMANN-NÖTZEL
Fabian Isensee
Pierre Jannin
Charles E. Jr. Kahn
Jens Kleesiek
Tahsin Kurc
Michal Kozubek
Bennett Landman … (voir 14 de plus)
GEERT LITJENS
Klaus Maier-Hein
Bjoern Menze
Henning Müller
Jens Petersen
Mauricio Reyes
Nicola Rieke
Bram Stieltjes
R. Summers
Sotirios A. Tsaftaris
Bram van Ginneken
Annette Kopp-Schneider
PAUL F. JÄGER
Lena Maier-Hein
2021-04-12
ArXiv (prépublication)
arxiv.org
arxiv.org
Maintenance of a collection of machines under partial observability: Indexability and computation of Whittle index
Nima Akbarzadeh
Aditya Mahajan
We consider the problem of scheduling maintenance for a collection of machines under partial observations when the state of each machine det… (voir plus)eriorates stochastically in a Markovian manner. We consider two observational models: first, the state of each machine is not observable at all, and second, the state of each machine is observable only if a service-person visits them. The agent takes a maintenance action, e.g., machine replacement, if he is chosen for the task. We model both problems as restless multi-armed bandit problem and propose the Whittle index policy for scheduling the visits. We show that both models are indexable. For the first model, we derive a closed-form expression for the Whittle index. For the second model, we propose an efficient algorithm to compute the Whittle index by exploiting the qualitative properties of the optimal policy. We present detailed numerical experiments which show that for multiple instances of the model, the Whittle index policy outperforms myopic policy and can be close-to-optimal in different setups.
2021-04-12
arXiv.org (prépublication)
dblp.uni-trier.de
Multi-tract multi-symptom relationships in pediatric concussion
Guido Ivan Guberman
Sonja Stojanovski
Eman Nishat
Alain Ptito
Danilo Bzdok
A. Wheeler
Maxime Descoteaux
The heterogeneity of white matter damage and symptoms in concussions has been identified as a major obstacle to therapeutic innovation. In c… (voir plus)ontrast, the vast majority of diffusion MRI studies on concussion have traditionally employed group-comparison approaches. Such studies do not consider heterogeneity of damage and symptoms in concussion. To parse concussion heterogeneity, the present study combines diffusion MRI (dMRI) and multivariate statistics to investigate multi-tract multi-symptom relationships. Using dMRI data from a sample of 306 children ages 9 and 10 with a history of concussion from the Adolescent Brain Cognitive Development Study (ABCD study), we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first capturing a mixture of patterns suggestive of microstructural complexity, the second representing almost exclusively axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 behavioural measures that capture well-known symptoms of concussions. We found idiosyncratic symptom-specific multi-tract connectivity features, which would not be captured in traditional univariate analyses. Multivariable connectome-symptom correspondences were stronger than all single-tract/single-symptom associations. Multi-tract connectivity features were also expressed equally across different sociodemographic strata and their expression was not accounted for by injury-related variables. In a replication dataset, the expression of multi-tract connectivity features predicted adverse psychiatric outcomes after accounting for other psychopathology-related variables. By defining cross-demographic multi-tract multi-symptom relationships to parse concussion heterogeneity, the present study can pave the way for the development of improved stratification strategies that may contribute to the success of future clinical trials and the improvement of concussion management.
2021-04-07
medRxiv (preprint)
doi.org
Safe option-critic: learning safety in the option-critic architecture
Arushi Jain
Khimya Khetarpal
Doina Precup
Abstract Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications b… (voir plus)ut also facilitates a better understanding of an agent’s decisions. We tackle this problem in the options framework (Sutton, Precup & Singh, 1999), a particular way to specify temporally abstract actions which allow an agent to use sub-policies with start and end conditions. We consider a behaviour as safe that avoids regions of state space with high uncertainty in the outcomes of actions. We propose an optimization objective that learns safe options by encouraging the agent to visit states with higher behavioural consistency. The proposed objective results in a trade-off between maximizing the standard expected return and minimizing the effect of model uncertainty in the return. We propose a policy gradient algorithm to optimize the constrained objective function. We examine the quantitative and qualitative behaviours of the proposed approach in a tabular grid world, continuous-state puddle world, and three games from the Arcade Learning Environment: Ms. Pacman, Amidar, and Q*Bert. Our approach achieves a reduction in the variance of return, boosts performance in environments with intrinsic variability in the reward structure, and compares favourably both with primitive actions and with risk-neutral options.
2021-04-07
The Knowledge Engineering Review (publié)
doi.org
arxiv.org
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
Vincent Dumoulin
Neil Houlsby
Utku Evci
Xiaohua Zhai
Ross Goroshin
Sylvain Gelly
Hugo Larochelle
Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art ad… (voir plus)vances in each family are measured largely in isolation of each other. As a result of diverging evaluation norms, a direct or thorough comparison of different approaches is challenging. To bridge this gap, we perform a cross-family study of the best transfer and meta learners on both a large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task Adaptation Benchmark, VTAB). We find that, on average, large-scale transfer methods (Big Transfer, BiT) outperform competing approaches on MD, even when trained only on ImageNet. In contrast, meta-learning approaches struggle to compete on VTAB when trained and validated on MD. However, BiT is not without limitations, and pushing for scale does not improve performance on highly out-of-distribution MD tasks. In performing this study, we reveal a number of discrepancies in evaluation norms and study some of these in light of the performance gap. We hope that this work facilitates sharing of insights from each community, and accelerates progress on few-shot learning.
2021-04-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Understanding Continual Learning Settings with Data Distribution Drift Analysis
Timothee LESORT
Massimo Caccia
Irina Rish
Classical machine learning algorithms often assume that the data are drawn i.i.d. from a stationary probability distribution. Recently, cont… (voir plus)inual learning emerged as a rapidly growing area of machine learning where this assumption is relaxed, i.e. where the data distribution is non-stationary and changes over time. This paper represents the state of data distribution by a context variable
2021-04-04
ArXiv (prépublication)
arxiv.org
arxiv.org
Discourse-Aware Unsupervised Summarization for Long Scientific Documents
Yue Dong
Andrei Mircea
Jackie Cheung
2021-04-01
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (publié)
doi.org
All in This Together? A Preregistered Report on Deservingness of Government Aid During the COVID-19 Pandemic
Aengus Bridgman
Eric Roman Owen Merkley
Peter John Loewen
Taylor Reid Owen
Derek Ruths
Abstract The COVID-19 pandemic has placed unprecedented pressure on governments to engage in widespread cash transfers directly to citizens … (voir plus)to help mitigate economic losses. Major and near-universal redistribution efforts have been deployed, but there is remarkably little understanding of where the mass public believes financial support is warranted. Using experimental evidence, we evaluate whether considerations related to deservingness, similarity, and prejudicial attitudes structure support for these transfers. A preregistered experiment found broad, generous, and nondiscriminatory support for direct cash transfers related to COVID-19 in Canada. The second study, accepted as a preregistered report, further probes these dynamics by comparing COVID-19-related outlays with nonemergency ones. We find that COVID-19-related spending was more universal as compared to a more generic cash allocation program. Given that the results were driven by the income of hypothetical recipients, we find broad support for disaster relief that is not means-tested or otherwise constrained by pre-disaster income.
2021-03-31
Journal of Experimental Political Science (published)
doi.org
All in This Together? A Preregistered Report on Deservingness of Government Aid During the COVID-19 Pandemic
Aengus Bridgman
Eric Merkley
Peter John Loewen
Taylor Owen
Derek Ruths
2021-03-31
Journal of Experimental Political Science (publié)
doi.org
Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework
Cécile Aenishaenslin
Barbara Häsler
André Ravel
E. Jane Parmley
Sarah Mediouni
Houda Bennani
Katharina D. C. Stärk
David Buckeridge
It is now widely acknowledged that surveillance of antimicrobial resistance (AMR) must adopt a “One Health” (OH) approach to successfull… (voir plus)y address the significant threats this global public health issue poses to humans, animals, and the environment. While many protocols exist for the evaluation of surveillance, the specific aspect of the integration of a OH approach into surveillance systems for AMR and antimicrobial Use (AMU), suffers from a lack of common and accepted guidelines and metrics for its monitoring and evaluation functions. This article presents a conceptual framework to evaluate the integration of OH in surveillance systems for AMR and AMU, named the Integrated Surveillance System Evaluation framework (ISSE framework). The ISSE framework aims to assist stakeholders and researchers who design an overall evaluation plan to select the relevant evaluation questions and tools. The framework was developed in partnership with the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). It consists of five evaluation components, which consider the capacity of the system to: [1] integrate a OH approach, [2] produce OH information and expertise, [3] generate actionable knowledge, [4] influence decision-making, and [5] positively impact outcomes. For each component, a set of evaluation questions is defined, and links to other available evaluation tools are shown. The ISSE framework helps evaluators to systematically assess the different OH aspects of a surveillance system, to gain comprehensive information on the performance and value of these integrated efforts, and to use the evaluation results to refine and improve the surveillance of AMR and AMU globally.
2021-03-24
Frontiers in Veterinary Science (publié)
doi.org