News
M. Ashok Kumar and Rajesh Sundaresan from the ECE department win the best paper award in NCC 2015!
 Details
 Start Date :20150304
 End date :00011130
[5th Mar, 15]: M. Ashok Kumar and Prof. Rajesh Sundaresan's paper titled "Relative αEntropy Minimizers Subject to Linear Statistical Constraints" has won the Best Paper Award in NCC 2015 held at IIT Bombay. Below is a briefing by M. Ashok on the findings of their paper.
 Congratulations Ashok and Prof. Rajesh! Can you state the highlights of the paper?
Relative αentropy is a parametric generalization of the usual relative entropy (or the KullbackLeibler divergence). Relative αentropy shares several interesting and useful properties with relative entropy. Motivated by the wellknown maximum entropy principle of statistical physics, we studied the problem of minimizing relative αentropy subject to certain moment constraints on the underlying probability distribution. We had two major contributions in the paper: (a) We explicitly found the minimizing probability distribution. The minimizer suggests a parametric family of probability distributions, which we call an αpowerlaw family. This αpower law family generalizes the wellknown exponential family in statistics. (b) We also explored the geometry associated with the minimizer.

How did you choose the problem addressed in the paper? Was it novel to you from the start itself or during the course of the work you realized its importance?
Rajesh Sundaresan (also the coauthor of the paper) identified the emergence of relative αentropy in the context of guessing and source coding. He also noticed that relative αentropy
is closely associated with Renyi/Tsallis entropy and that it shares
several interesting properties with the usual relative entropy. When I
joined him, he suggested me to explore these further. Later, during the
course of time, we also came to know the relevance of relative αentropy in a statistical estimation problem.
 Having come this far, now how do you see the impact of these findings?
I can see two possible impacts. One, the αpower
law distributions that we identified can be useful for statistical
modelling purposes. Second, the geometry associated with the αpower
law family may be helpful in reducing the complexity involved in a
statistical estimation problem (this particular problem is not addressed
in the current paper though).
 What’s next from here? I assume you are waiting for your PhD defense.
Yes, of course I do! The next thing would be to explore more on the usefulness of the tools developed in my thesis, especially the geometry we explored, in simplifying certain statistical estimation problems.
Thank you very much!
Dept. ECE, IISc Banaglore.