In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...
We investigate the design of algorithms resilient to memory faults, i.e., algorithms that, despite the corruption of some memory values during their execution, are able to produce...
This paper considers the development of a general framework for the analysis of transmit beamforming methods in multiple antenna systems with finite-rate feedback. Inspired by the...
: We initiate the study of local, sublinear time algorithms for finding vertices with extreme topological properties -- such as high degree or clustering coefficient -- in large so...
We study data structures in the presence of adversarial noise. We want to encode a given object in a succinct data structure that enables us to efficiently answer specific queries...