Tata Institute of Fundamental Research
Polynomial Learning of Distribution Families
Seminar
Speaker:
Kaushik Sinha
Department of Computer Science and
Engineering
The Ohio State University
Enarson Hall 154 W 12t
Date:
Friday, 3 Dec 2010 (all day)
Venue:
A-212 (STCS Seminar Room)
(Scan to add to calendar)
Abstract:
The study of Gaussian mixture distributions goes back to the late 19th century, when Pearson introduced the method of moments to analyze the statistics of a crab population. They have since become one of the most popular tools of modeling and data analysis, extensively used in speech recognition, computer vision and other fields. Yet their properties are still not well understood.
In my talk I will discuss some theoretical aspects of the problem of learning Gaussian mixtures. In particular, I will discuss our recent result with Mikhail Belkin, which, in a certain sense, completes work on an active recent topic in theoretical computer science by establishing quite general conditions for polynomial learnability of mixture distributions.
| Speaker: | Kaushik Sinha Department of Computer Science and Engineering The Ohio State University Enarson Hall 154 W 12t |
| Date: | Friday, 3 Dec 2010 (all day) |
| Venue: | A-212 (STCS Seminar Room) |
(Scan to add to calendar)
Abstract:
The study of Gaussian mixture distributions goes back to the late 19th century, when Pearson introduced the method of moments to analyze the statistics of a crab population. They have since become one of the most popular tools of modeling and data analysis, extensively used in speech recognition, computer vision and other fields. Yet their properties are still not well understood.
In my talk I will discuss some theoretical aspects of the problem of learning Gaussian mixtures. In particular, I will discuss our recent result with Mikhail Belkin, which, in a certain sense, completes work on an active recent topic in theoretical computer science by establishing quite general conditions for polynomial learnability of mixture distributions.
In my talk I will discuss some theoretical aspects of the problem of learning Gaussian mixtures. In particular, I will discuss our recent result with Mikhail Belkin, which, in a certain sense, completes work on an active recent topic in theoretical computer science by establishing quite general conditions for polynomial learnability of mixture distributions.