—A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. How...
The problem of computing low rank approximations of matrices is considered. The novel aspect of our approach is that the low rank approximations are on a collection of matrices. W...
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
Standard concurrency control mechanisms offer a trade-off: Transactional memory approaches maximize concurrency, but suffer high overheads and cost for retrying in the case of act...
Yannis Smaragdakis, Anthony Kay, Reimer Behrends, ...
A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow membership queries on a set that can be changing dynamically via insertions and deletions. As...
Flavio Bonomi, Michael Mitzenmacher, Rina Panigrah...