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SUMMARY:Distributed Architectures for Machine Learning
DTSTART:20160701T120000
DTEND:20160701T140000
DTSTAMP:20260407T065907Z
UID:5064ea319cfc98da56af517fee81e5e090b2f8fa9d70625d13ac44b7
CATEGORIES:Conferences - Seminars
DESCRIPTION:Georgios Damaskinos\nEDIC Candidacy Exam\nExam President: Prof
 . Karl Aberer\nThesis Director: Prof. Rachid Guerraoui\nCo-examiner: Prof.
  Boi Faltings\nBackground papers:Tencent Rec: Real-time Stream Recommendat
 ion in\nPractice HyRec: Leveraging Browsers for Scalable\nRecommendersDist
 ributed Asynchronous online learning for natural language processingAbstra
 ct\nThe big data era highlights a major scalability challenge for online m
 achine learning. Addressing this challenge requires a holistic approach th
 at involves distributed solutions both from the algorithmic and from the s
 ystem perspective. In this proposal\, we first study two scalable recommen
 der systems with different architectures along with multiple machine learn
 ing algorithms. TENCENTREC is a real-time scalable recommender with a cent
 ralized framework. HYREC is an online recommender that employs a hybrid ar
 chitecture. We then study an asynchronous algorithm with promising charact
 eristics towards designing scalable online machine learning algorithms. In
  this proposal\, we argue that a democratized machine learning system must
  enable all service providers to deploy their service and all clients to u
 se it regardless of their resources. Finally\, we examine recent work conc
 erning general purpose large-scale machine learning frameworks and propose
  future work towards democratizing machine learning.
LOCATION:BC 329 https://plan.epfl.ch/?room==BC%20329
STATUS:CONFIRMED
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