In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integratin...
—In this paper we discuss the notion of situated evolution. Our treatment includes positioning situated evolution on the map of evolutionary processes in terms of time- and space...
In high-dimensional query processing, the optimization of the logical page-size of index structures is an important research issue. Even very simple query processing techniques suc...
We study graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical models. For density estim...
Anupam Gupta, John D. Lafferty, Han Liu, Larry A. ...