Discover Top Posts Tagged with #michael kearns

Kearns, activists say foreclosure on first ‘bank shaming’ house is complete

While holding individuals accountable within the city can be easy, particularly those in poverty, it is an altogether different matter when trying to hold a powerful corporation accountable. Oftentimes complex laws, paid-off politicians, and teams of lawyers can get in the way of the community insisting that a corporation be beholden to its responsibilities. Yet, last week a local group did just that by successfully implementing a “bank shaming” campaign.

Led by State Assemblyman Michael Kearns a group of activists pressured Bank of America to foreclose a house by using a sign that said “Shame On You Bank of America For Not Completing the Foreclosure Process.” The house, located on Sidway Street in the Old First Ward, has been stuck in the bureaucratic “vacancy vortex” limbo and contributing to Buffalo’s “zombie neighborhoods.”

The campaign caught the attention of Bank of America who completed the process leading the team to replace the “shaming” sign to a “Thank You for being a Good Neighbor” sign. Buffalo has seen a huge influx of cheap home buyers in the last few years who are willing to restore such houses given the opportunity to purchase them and banks not fulfilling their responsibilities is one hurdle that has been slowing this process of revitalization.

#Buffalo #Buffalo housing #Buffalo revitalization #Buffalo banks #Michael Kearns #Bank shaming

An Introduction to Computational Learning Theory

An Introduction to Computational Learning Theory Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs.The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

#computational learning theory #theoretical computer science #michael kearns #statistical queries #finite automata #precise arguments #computational efficiency #computational limitations