SEMINAR NOTICE

Conformal Prediction for Reliable Machine Learning

Dr. Vineet N Balasubramanian, IITH

DATE & TIME : 10 October 2014, 4.00 PM

VENUE: Semianr Room, SCIS

Abstract

Reliable estimation of confidence remains a significant challenge as learning algorithms proliferate into challenging real-world pattern recognition applications. The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness, transductive inference and hypothesis testing, and has several desirable properties for potential use in various real-world applications, such as the calibration of the obtained confidence values in an online setting. Further, this framework can be applied across all existing classification and regression methods (such as neural networks, Support Vector Machines, ridge regression, etc), thus making it a very generalizable approach. Over the last few years, there has been a growing interest in applying this framework to real-world problems such as clinical decision support, medical diagnosis, sea surveillance, network traffic classification, and face recognition. This talk will describe the basic theory of the framework, demonstrate examples of how the framework can be applied in real-world problems, and also explain our work on improving the efficiency of the framework using Multiple Kernel Learning.