Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations
Elliot Layne
Jason Hartford
Sébastien Lachapelle
Mathieu Blanchette
Dhanya Sridhar
Recently, learning invariant predictors across varying environments has been shown to improve the generalization of supervised learning meth… (see more)ods. This line of investigation holds great potential for application to biological problem settings, where data is often naturally heterogeneous. Biological samples often originate from different distributions, or environments. However, in biological contexts, the standard "invariant prediction" setting may not completely fit: the optimal predictor may in fact vary across biological environments. There also exists strong domain knowledge about the relationships between environments, such as the evolutionary history of a set of species, or the differentiation process of cell types. Most work on generic invariant predictors have not assumed the existence of structured relationships between environments. However, this prior knowledge about environments themselves has already been shown to improve prediction through a particular form of regularization applied when learning a set of predictors. In this work, we empirically evaluate whether a regularization strategy that exploits environment-based prior information can be used to learn representations that better disentangle causal factors that generate observed data. We find evidence that these methods do in fact improve the disentanglement of latent embeddings. We also show a setting where these methods can leverage phylogenetic information to estimate the number of latent causal features.
2022-07-09
auai.org/UAI/2022/Workshop/CRL (poster)
doi.org
openreview.net
Partial Disentanglement via Mechanism Sparsity
Sébastien Lachapelle
Simon Lacoste-Julien
2022-07-09
auai.org/UAI/2022/Workshop/CRL (oral)
doi.org
openreview.net
FIXME: synchronize with database! An empirical study of data access self-admitted technical debt
Biruk Asmare Muse
Csaba Nagy
Anthony Cleve
Foutse Khomh
Giuliano Antoniol
2022-07-08
Empirical Software Engineering (published)
doi.org
arxiv.org
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
B. 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.
2022-07-07
medRxiv (preprint)
doi.org
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu
Bhaskar Mitra
Chen Ma
Fernando Diaz
Xue (Steve) 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.
2022-07-07
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.
2022-07-07
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.
2022-07-07
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.
2022-07-07
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
Citation: Dubé L, Silveira PP, Nielsen DE, Moore S, Paquet C, Cisneros-Franco JM, Kemp G, Knauper B, Ma Y, Khan M, Bartlett-Esquilant G, Ev… (see more)ans AC, Fellows LK, Armony JL, Spreng RN, Nie J-Y, Brown ST, Northoff G and Bzdok D (2022) From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation. Front. Public Health 10:720117. doi: 10.3389/fpubh.2022.720117 From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation
2022-07-05
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 Fung
2022-07-05
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 Cheung
2022-07-01
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 Fung
Philippe Charland
2022-07-01
Information Sciences (published)
doi.org