LTSPD

Learning and Testing Structured Probability Distributions

Coordinatore	THE UNIVERSITY OF EDINBURGH    more  Organization address address: OLD COLLEGE, SOUTH BRIDGE city: EDINBURGH postcode: EH8 9YL contact info Titolo: Ms. Nome: Angela Cognome: Noble Email: send email Telefono: +44 131 650 59024 Fax: +44 131 651 4028
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	100˙000 €
EC contributo	100˙000 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2013-CIG
Funding Scheme	MC-CIG
Anno di inizio	2014
Periodo (anno-mese-giorno)	2014-03-01   -   2018-02-28

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	THE UNIVERSITY OF EDINBURGH  Organization address address: OLD COLLEGE, SOUTH BRIDGE city: EDINBURGH postcode: EH8 9YL contact info Titolo: Ms. Nome: Angela Cognome: Noble Email: send email Telefono: +44 131 650 59024 Fax: +44 131 651 4028	UK (EDINBURGH)	coordinator	100˙000.00

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 Obiettivo del progetto (Objective)

'The research topic of the current proposal lies within the area of Algorithms and Complexity. The goal of this proposal is to advance a research program of developing computationally efficient algorithms for learning and testing a wide range of natural and important classes of probability distributions.

We live in an era of “big data,” where the amount of data that can be brought to bear on questions of biology, climate, economics, etc, is vast and expanding rapidly. Much of this raw data frequently consists of example points without corresponding labels. The challenge of how to make sense of this unlabeled data has immediate relevance and has rapidly become a bottleneck in scientific understanding across many disciplines.

An important class of big data is most naturally modeled as samples from a probability distribution over a very large domain. This prompts the basic question: Given samples from some unknown distribution, what can we infer? While this question has been studied for several decades by various different communities of researchers, both the number of samples and running time required for such estimation tasks are not yet well understood, even for some surprisingly simple types of discrete distributions.

In this project we will develop computationally efficient algorithms for learning and testing various classes of discrete distributions over very large domains. Specific problems we will address include: (1) Developing efficient algorithms to learn and test probability distributions that satisfy various natural types of 'shape restrictions' on the underlying probability density function. (2) Developing efficient algorithms for learning and testing complex distributions that result from the aggregation of many independent simple sources of randomness.

We believe that highly efficient algorithms for these estimation tasks may play an important role for the next generation of large-scale machine learning applications.'