In this paper we study the long standing problem of information extraction from multiple linear approximations. We develop a formal statistical framework for block cipher attacks b...
The relative tolerances for interconnect and device parameter variations have not scaled with feature sizes which have brought about significant performance variability. As we sca...
We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). O...
Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is ...
We consider a system of linear constraints over any finite Abelian group G of the following form: i(x1, . . . , xn) ≡ i,1x1 + · · · + i,nxn ∈ Ai for i = 1, . . . , t and e...