We propose a general regularized empirical risk minimization framework for sparse learning which accommodates popular regularizers such as lasso, group lasso, and the trace norm. Within this framework, we develop two optimization algorithms. The first method is based on squared penalties added to the empirical risk and is solved using a subgradient-based L-BFGS quasi-Newton method. The second m...

We describe an automated technique for assume-guarantee style checking of strong simulation between a system and a specification, both expressed as non-deterministic Labeled Probabilistic Transition Systems (LPTSes). We first characterize counterexamples to strong simulation as stochastic trees and show that simpler structures are insufficient. Then, we use these trees in an abstraction refinem...

Due to enormous resource consumption, model checking each revision of evolving systems repeatedly is impractical. To reduce cost in checking every revision, contextual assumptions are reused from assumeguarantee reasoning. However, contextual assumptions are not always reusable. We propose a fine-grained learning technique to maximize the reuse of contextual assumptions. Based on fine-grained l...

Given a coloring of the edges of a multi-hypergraph, a rainbow t-matching is a collection of t disjoint edges, each having a different color. In this note we study the problem of finding a rainbow t-matching in an r-partite r-uniform multi-hypergraph whose edges are colored with f colors such that every color class is a matching of size t. This problem was posed by Aharoni and Berger, who asked...