Natural language research announcements

["Samdani, Rajhans" <rsamdan2 AT illinois.edu>]

Dear all,

This is a gentle reminder for today's AIIS talk by Prof. Maxim Raginsky  
(http://www.ece.illinois.edu/directory/profile.asp?maxim) from the ECE 
department. Details follow.

When: Friday, April 20, 2 pm.

Where: 3405, Siebel Center.

Title:
Fundamental Limits of Passive and Active Learning


Abstract:
Statistical learning theory is concerned with making accurate predictions on 
the basis of past observations. One of the main characteristics of any 
learning problem is its sample complexity: the minimum number of observations 
needed to ensure a given prediction accuracy at a given confidence level. For 
the most part, the focus has been on passive learning, in which the learning 
agent receives independent training samples. However, recently there has been 
increasing interest in active learning, in which past observations are used 
to control the process of gathering future observations. The main question is 
whether active learning is strictly more powerful than its passive 
counterpart. One way to answer this is to compare the sample complexities of 
passive and active learning for the same accuracy and confidence.

In this talk, based on joint work with Sasha Rakhlin (Department of 
Statistics, University of Pennsylvania), I will present a new unified 
approach to deriving tight lower bounds on the sample complexity of both 
passive and active learning in the setting of binary classification. This 
approach is fundamentally rooted in information theory, in particular, the 
simple but powerful data processing inequality for the f-divergence. I will 
give a high-level overview of the proof technique and discuss the connections 
between active learning and hypothesis testing with feedback.

Hoping to see y'all,
Rajhans