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.

BIO

Vineeth N Balasubramanian is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Hyderabad, India. Until July 2013, he was an Assistant Research Professor at the Center for Cognitive Ubiquitous Computing (CUbiC) at Arizona State University (ASU). He holds dual Masters degrees in Mathematics (2001) and Computer Science (2003) from Sri Sathya Sai Institute of Higher Learning, India, and worked at Oracle Corporation until 2005. His PhD dissertation (2010) on the Conformal Predictions framework was nominated for the Outstanding PhD Dissertation at the Department of Computer Science at ASU. He was also awarded the Gold Medals for Academic Excellence in the Bachelors program in Math in 1999, and for his Masters program in Computer Science in 2003. His research interests include pattern recognition, machine learning, computer vision and multimedia computing within assistive and healthcare applications. He has over 40 research publications in premier peer-reviewed venues, 3 patents under review, and has received research grants from the US National Science Foundation in these fields. He is a member of the IEEE, ACM and AAAI.