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Dimitrakakis, Christos

Nom
Dimitrakakis, Christos
Affiliation principale
Fonction
Professor
Email
christos.dimitrakakis@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/30936
_
0000-0002-5367-5189

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  • Publication
    Accès libre
    On The Differential Privacy of Thompson Sampling With Gaussian Prior
    (2018-06-24T18:37:09Z)
    Aristide C. Y. Tossou
    ;
    Dimitrakakis, Christos 
    We show that Thompson Sampling with Gaussian Prior as detailed by Algorithm 2 in (Agrawal & Goyal, 2013) is already differentially private. Theorem 1 show that it enjoys a very competitive privacy loss of only O(ln2T) after T rounds. Finally, Theorem 2 show that one can control the privacy loss to any desirable ϵ level by appropriately increasing the variance of the samples from the Gaussian posterior. And this increases the regret only by a term of O(ln2Tϵ). This compares favorably to the previous result for Thompson Sampling in the literature ((Mishra & Thakurta, 2015)) which adds a term of O(Kln3Tϵ2) to the regret in order to achieve the same privacy level. Furthermore, our result use the basic Thompson Sampling with few modifications whereas the result of (Mishra & Thakurta, 2015) required sophisticated constructions.
  • Publication
    Accès libre
    Learning to Match
    (2017-07-30T21:50:50Z)
    Philip Ekman
    ;
    Sebastian Bellevik
    ;
    Dimitrakakis, Christos 
    ;
    Aristide C. Y. Tossou
    Outsourcing tasks to previously unknown parties is becoming more common. One specific such problem involves matching a set of workers to a set of tasks. Even if the latter have precise requirements, the quality of individual workers is usually unknown. The problem is thus a version of matching under uncertainty. We believe that this type of problem is going to be increasingly important. When the problem involves only a single skill or type of job, it is essentially a type of bandit problem, and can be solved with standard algorithms. However, we develop an algorithm that can perform matching for workers with multiple skills hired for multiple jobs with multiple requirements. We perform an experimental evaluation in both single-task and multi-task problems, comparing with the bounded $\epsilon$-first algorithm, as well as an oracle that knows the true skills of workers. One of the algorithms we developed gives results approaching 85\% of oracle's performance. We invite the community to take a closer look at this problem and develop real-world benchmarks.