For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the tra...
Transferring knowledge from one domain to another is challenging due to a number of reasons. Since both conditional and marginal distribution of the training data and test data ar...
Constructing correct distributed systems from their high-level models has always been a challenge and often subject to serious errors because of their non-deterministic and non-at...
Borzoo Bonakdarpour, Marius Bozga, Mohamad Jaber, ...
We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for t...
Abstract -- Detection of execution anomalies is very important for the maintenance, development, and performance refinement of large scale distributed systems. Execution anomalies ...