In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns proper...
During the last decade, software transactional memory (STM) gained wide popularity in many areas of parallel computing. In this paper, we introduce LISP-derived language equipped ...
We address a new learning problem where the goal is to build a predictive model that minimizes prediction time (the time taken to make a prediction) subject to a constraint on mod...
Biswanath Panda, Mirek Riedewald, Johannes Gehrke,...
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
We present the implementation and evaluation of a penalized alternating minimization (AM) method1 for the computation of a specimen's complex transmittance function (magnitud...