Publications

Advanced MRI scan acquisition metrics improve baseline disease severity predictions compared to traditional community MRI scan metrics
Abdul Al-Shawwa
Kalum Ost
David W. Cadotte
David Anderson
Nathan Evaniew
Nathan Evaniew
Bradley Jacobs
Julien Cohen‐Adad
Degenerative Cervical Myelopathy (DCM) is the functional derangement of the spinal cord and acts as one of the most common atraumatic spinal… (see more) cord injuries. Magnetic resonance imaging (MRI) are key in confirming the diagnosis of DCM in patients, though the utilization of higher fidelity magnetic resonance imaging scans and their integration into machine learning models remains largely unexplored. This study looks at the predictive ability of common community MRI scans in comparison to high fidelity scans in disease diagnosis. We hypothesize that the utilization of higher fidelity "advanced" MRI scans will increase the effectiveness of machine learning models predicting DCM severity. Through the utilization of Random Forest Classifiers, we have been able to predict disease severity with 41.8% accuracy in current community MRI scans and 63.9% in the advanced MRI scans. Furthermore, across the different predictive model variations tested, the advanced MRI scans consistently produced higher prediction accuracies compared to the community MRI counterparts. These results support our hypothesis and indicate that machine learning models have the potential to predict disease severity. However, neither performed well enough to be considered for use in clinical practice, indicating that the utilization of more sophisticated machine models may be required for these purposes.
2022-07-06
medRxiv (preprint)
doi.org
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu
Bhaskar Mitra
Chen Ma
Fernando Diaz
Xue Liu
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or… (see more) groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users, has received relatively less attention. However, such systemic disparities in information exposure can result in observable social harms, such as withholding economic opportunities from historically marginalized groups (allocative harm) or amplifying gendered and racialized stereotypes (representational harm). Previously, Diaz et al. developed the expected exposure metric---that incorporates existing user browsing models that have previously been developed for information retrieval---to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. Furthermore, we study and discuss the relationships between the different exposure fairness dimensions proposed in this paper, as well as demonstrate how stochastic ranking policies can be optimized towards said fairness goals.
2022-07-06
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (published)
doi.org
arxiv.org
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Filip Radlinski
Krisztian Balog
Fernando Diaz
Lucas Dixon
Ben Wedin
Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the c… (see more)hallenge of supporting people's understanding and control of these systems and explore a fundamentally new way of thinking about representation of knowledge in recommendation and personalization systems. Specifically, we argue that it may be both desirable and possible for algorithms that use natural language representations of users' preferences to be developed. We make the case that this could provide significantly greater transparency, as well as affordances for practical actionable interrogation of, and control over, recommendations. Moreover, we argue that such an approach, if successfully applied, may enable a major step towards systems that rely less on noisy implicit observations while increasing portability of knowledge of one's interests.
2022-07-06
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (published)
doi.org
arxiv.org
Retrieval-Enhanced Machine Learning
Hamed Zamani
Fernando Diaz
Mostafa Dehghani
Donald Metzler
Michael Bendersky
Although information access systems have long supportedpeople in accomplishing a wide range of tasks, we propose broadening the scope of use… (see more)rs of information access systems to include task-driven machines, such as machine learning models. In this way, the core principles of indexing, representation, retrieval, and ranking can be applied and extended to substantially improve model generalization, scalability, robustness, and interpretability. We describe a generic retrieval-enhanced machine learning (REML) framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. The REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
2022-07-06
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (published)
doi.org
arxiv.org
Offline Retrieval Evaluation Without Evaluation Metrics
Fernando Diaz
Andres Ferraro
Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scala… (see more)r metric such as average precision or normalized discounted cumulative gain. We can use this metric to compare the performance of multiple systems for the same request. Although evaluation metrics provide a convenient summary of system performance, they also collapse subtle differences across users into a single number and can carry assumptions about user behavior and utility not supported across retrieval scenarios. We propose recall-paired preference (RPP), a metric-free evaluation method based on directly computing a preference between ranked lists. RPP simulates multiple user subpopulations per query and compares systems across these pseudo-populations. Our results across multiple search and recommendation tasks demonstrate that RPP substantially improves discriminative power while correlating well with existing metrics and being equally robust to incomplete data.
2022-07-05
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (published)
doi.org
arxiv.org
From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation
Laurette Dubé
Patricia P. Silveira
Daiva E. Nielsen
Spencer Moore
Catherine Paquet
J. Miguel Cisneros-Franco
Gina Kemp
Bärbel Knauper
Yu Ma
Mehmood Khan
Gillian Bartlett-Esquilant
Alan C. Evans
Lesley K. Fellows
Jorge L. Armony
R. Nathan Spreng
Jian-Yun Nie
Shawn T. Brown
Georg Northoff
Danilo Bzdok
2022-07-04
Frontiers in Public Health (published)
doi.org
Incentivized Security-Aware Computation Offloading for Large-Scale Internet of Things Applications
Talal Halabi
Adel Abusitta
Glaucio H.S. Carvalho
Benjamin C. M. Fung
2022-07-04
2022 7th International Conference on Smart and Sustainable Technologies (SpliTech) (published)
doi.org
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge
Ian Porada
Alessandro Sordoni
Jackie CK Cheung
2022-06-30
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (published)
doi.org
arxiv.org
DyAdvDefender: An instance-based online machine learning model for perturbation-trial-based black-box adversarial defense
Miles Q. Li
Benjamin C. M. Fung
Philippe Charland
2022-06-30
Information Sciences (published)
doi.org
Exploring the roles of artificial intelligence in surgical education: A scoping review
Elif Bilgic
Andrew Gorgy
Alison Yang
Michelle Cwintal
Hamed Ranjbar
Kalin Kahla
Dheeksha Reddy
Kexin Li
Helin Ozturk
Eric Zimmermann
Andrea Quaiattini
S. A. Rahimi
Dan Poenaru
Jason M. Harley
2022-06-30
The American Journal of Surgery (published)
doi.org
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
François-Xavier Devailly
Denis Larocque
Laurent Charlin
Scaling adaptive traffic signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning atte… (see more)mpts to address this challenge by distributing control to specialized agents. However, specialization hinders generalization and transferability, and the computational graphs underlying neural-network architectures—dominating in the multi-agent setting—do not offer the flexibility to handle an arbitrary number of entities which changes both between road networks, and over time as vehicles traverse the network. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks which adapts to the structure of any road network, to learn detailed representations of traffic signal controllers and their surroundings. Our decentralized approach enables learning of a transferable-adaptive-traffic-signal-control policy. After being trained on an arbitrary set of road networks, our model can generalize to new road networks and traffic distributions, with no additional training and a constant number of parameters, enabling greater scalability compared to prior methods. Furthermore, our approach can exploit the granularity of available data by capturing the (dynamic) demand at both the lane level and the vehicle level. The proposed method is tested on both road networks and traffic settings never experienced during training. We compare IG-RL to multi-agent reinforcement learning and domain-specific baselines. In both synthetic road networks and in a larger experiment involving the control of the 3,971 traffic signals of Manhattan, we show that different instantiations of IG-RL outperform baselines.
2022-06-30
IEEE Transactions on Intelligent Transportation Systems (published)
doi.org
arxiv.org
Leishmania parasites exchange drug-resistance genes through extracellular vesicles
Noélie Douanne
George Dong
Atia Amin
Lorena Bernardo
Mathieu Blanchette
David Langlais
Martin Olivier
Christopher Fernandez-Prada
2022-06-30
Cell Reports (published)
doi.org