When looking for similar objects, like images and documents, and especially when querying a large remote data-base for similar objects, it is often useful to construct short similarity-preserving binary hashes. That is, to map each image or document to a short bit strings such that similar objects have similar bit strings. Such a mapping lies at the root of nearest neighbor search methods such as Locality Sensitive Hashing (LSH) and is recently gaining popularity in a variety of vision, image retrieval and document retrieval applications. In this talk I will demonstrate, both theoretically and empirically, that even for symmetric and well behaved similarity measures, much could be gained by using two different hash functions---one for hashing objects in the database and an entirely different hash function for the queries. Such asymmetric hashings can allow to significantly shorter bit strings and more accurate retrieval.

Joint work with Behnam Neyshabur, Yury Makarychev and Russ Salakhutdinov 

Nati Srebro obtained his PhD at the Massachusetts Institute of Technology (MIT) in 2004, held a post-doctoral fellowship with the Machine Learning Group at the University of Toronto, and was a Visiting Scientist at IBM Haifa Research Labs.  Since January 2006, he has been on the faculty of the Toyota Technological Institute at Chicago (TTIC) and the University of Chicago, and has also served as the first Director of Graduate Studies at TTIC.  From 2013 to 2014 he was associate professor at the Technion-Israel Institute of Technology. Prof. Srebro's research encompasses methodological, statistical and computational aspects of Machine Learning, as well as related problems in Optimization.  Some of Prof. Srebro's significant contributions include work on learning "wider" Markov networks, pioneering work on matrix factorization and collaborative prediction, including introducing the use of the nuclear norm for machine learning and matrix reconstruction and work on fast optimization techniques for machine learning, and on the relationship between learning and optimization.