In the last few decades, there has been considerable progress in the understanding of binary classification (learning of binary-valued functions) and regression (learning of real-valued functions), both classical problems in mach

We will try to answer the above question by analyzing the stopping times (which is the time after which the deck of cards is completely random) of the card shuffling process.

A boolean function f on N variables is called evasive if its decision tree complexity is N, i.e., one must query *all* the variables (in worst case) in order to decide if f(X) = 1.

I propose to give a series of lectures explaining the recent paper by Rahul Jain, Zhengfeng Ji, Sarvagya Upadhyay and John Watrous showing that the class of problems having quantum interactive proofs is the same as the class of p

Lately, there has been a considerable amount of interest in design methodologies for embedded systems that are specifically targeted towards stream processing, e.g., audio/video applications and control applications processing se