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Privacy-preserving and Personalized Federated Machine Learning for Medical Data

Publications associées (32)

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Combine and Conquer

Vincent Etter

In this thesis, we explore the application of data mining and machine learning techniques to several practical problems. These problems have roots in various fields such as social science, economics, and political science. We show that computer science tec ...

EPFL2015

Scalable Collaborative Bayesian Preference Learning

Matthias Seeger, Mohammad Emtiyaz Khan, Young Jun Ko

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, ...

2014

A Probabilistic Approach to Mining Mobile Phone Data Sequences

Daniel Gatica-Perez, Katayoun Farrahi

We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suff ...

2014

Same Queries, Different Data: Can we Predict Query Performance?

Anastasia Ailamaki, Miguel Sérgio De Oliveira Branco, Adrian Daniel Popescu

We consider MapReduce workloads that are produced by analytics applications. In contrast to ad hoc query workloads, analytics applications are comprised of fixed data flows that are run over newly arriving data sets or on different portions of an existing ...

2012

Personalized Modeling for Drug Concentration Prediction Using Support Vector Machine

Giovanni De Micheli, Wenqi You Dubout

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propos ...

2011

Open-Ended Learning of Visual and Multi-Modal Patterns

Jie Luo

A common trend in machine learning and pattern classification research is the exploitation of massive amounts of information in order to achieve an increase in performance. In particular, learning from huge collections of data obtained from the web, and us ...

EPFL2011

Open-ended Learning of Visual and Multi-modal Patterns

Jie Luo

Ecole polytechnique fédérale de Lausanne2011

Data utility modelling for mismatch reduction

In the "missing data" (MD) approach to noise robust automatic speech recognition (ASR), speech models are trained on clean data, and during recognition sections of spectral data dominated by noise are detected and treated as "missing". However, this all-or ...

2001

Data utility modelling for mismatch reduction

IDIAP2001

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000