Model compensation schemes are a powerful approach to handling mismatches between training and testing conditions. Normally these schemes are run in a batch adaptation mode, re-re...
We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is s...
Kun Deng, Chris Bourke, Stephen D. Scott, Julie Su...
We propose a model for describing the parallel performance of multigrid software on distributed memory architectures. The goal of the model is to allow reliable predictions to be m...
In this paper, we study shelf space allocation optimization which is important to retail operations management. Our approach is to formulate a model that is applicable to operatio...
Andrew Lim, Brian Rodrigues, Fei Xiao, Xingwen Zha...
Many formal models of cognition implicitly use subjective probability distributions to capture the assumptions of human learners. Most applications of these models determine these...