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- Optimization in Machine Learning: An Overview
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- Home
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- Study with us
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-
Student life & resources
Postgraduate research
- Info for new students
- Current research students
- Postgraduate conference
- Postgraduate events
- Postgraduate student awards
- Michael Tallis PhD Research Travel Award
- Information about research theses
- Past research students
- Resources
- Entry requirements
- PhD projects
- Obtaining funding
- Application & fee information
Student services
- Help for postgraduate students
- Thesis guidelines
- School assessment policies
- Computing information
- Mathematics Drop-in Centre
- Consultation
- Statistics Consultation Service
- Academic advice
- Enrolment variation
- Changing tutorials
- Illness or misadventure
- Application form for existing casual tutors
- ARC grants Head of School sign off
- Computing facilities
- Choosing your major
- Engage with us
- News & events
- Contact
Abstract:
Optimization is proving to be a vital tool in machine learning, as a means to formulate and solve problems in the area. Recently, the complex challenges posed by large data sets and the demands for a greater variety of analysis and learning tasks have brought a wider range of optimization tools into play. Among these tools are stochastic gradient methods, sparse optimization methods, enhanced first-order methods, coordinate descent, and approximate second-order methods. In this talk, we survey a variety of problem formulations and outline the optimization techniques that are relevant in each case, highlighting some recent developments in such areas as parallel stochastic gradient descent.
Speaker
Professor Stephen Wright
Research Area
Applied Seminar
Affiliation
University of Wisconsin-Madison
Date
Mon, 28/05/2012 - 3:00pm to 4:00pm
Venue
RC-4082