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Illapha Cuba Gyllensten

dblp:116/3238 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-9241-3763ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Efficient and distributed learning · 100%
Databases, data mining, and information retrieval
1 paper
Recommender systems · 100%
Network and information security
1 paper
Privacy and data protection · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
federated learning		 0.412019
Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization · MobiSys 2019
Machine learning › Efficient and distributed learning › edge computing › on-device machine learning
on-device learning		 0.412019
Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization · MobiSys 2019
Recommender systems › collaborative filtering › matrix factorization
federated matrix factorization		 0.412019
Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization · MobiSys 2019
Recommender systems › collaborative filtering
matrix factorization		 0.412019
Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization · MobiSys 2019
Privacy and data protection › privacy-preserving machine learning
privacy-preserving recommendation		 0.112019
Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization · MobiSys 2019

Methods — techniques the papers use, named apart from their topics

matrix factorization · 1.1federated learning · 1.1
YearPublicationVenuePosition
2019 Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization
abstract
Recommender systems have gained prominence in bringing users tailor-made content from the web aiding their decision making process. However, this personalization comes at a cost of privacy of sharing personal information with the recommendation provider. Moreover, with growing sizes of datasets and models, centralized processing of such data has become challenging. To this end, we propose a federated matrix factorization algorithm to enable personal data to be stored and used on-device for training while sending updates to train a centralized model. We illustrate preliminary results from our algorithm applied to recommendation of articles to users of a mobile application based on their reading history. We compare our performance with centralized matrix factorization applied on the same dataset.
Koustabh Dolui, Illapha Cuba Gyllensten, Dietwig Lowet, Sam Michiels, Hans Hallez, Danny Hughes 0001
 MobiSys2
2019 Changes in Daily Measures of Blood Pressure and Heart Rate Improve Weight-Based Detection of Heart Failure Deterioration in Patients on Telemonitoring
abstract
Blood pressure (BP) and heart rate (HR) are often captured in conjunction with weight in telemonitoring systems, but the additional prognostic potential of daily measurements of BP and HR in providing information on upcoming hospitalizations for worsening heart failure (HFH) have not been explored thoroughly. We retrospectively analyzed 267 daily home-telemonitored heart failure (HF) subjects. We extracted those episodes of HFHs that had sufficient data entries in the days leading up to hospitalization and tested the prognostic potential of 48 trend features based on weight, systolic BP, diastolic BP, pulse pressure (PP), and HR with a Naïve Bayesian model. The single best-performing trend feature-with a cross-validated estimate of 0.64 for the area under the curve (AUC) with a standard deviation (SD) of 0.01-is based on a 2-day weight trend. The best multivariate feature set (cross-validated [Formula: see text], [Formula: see text]) comprises of 2-day trend features based on weight, systolic BP, and HR. There were large variations in the weight trends preceding hospitalizations and weight change alone had a modest predictive ability. Readily interpretable features capturing trends in BP and HR provided additional prognostic information and can be used for improving classification.
Rohan Joshi, Illapha Cuba Gyllensten
 IEEE J. Biomed. Health Informatics2