We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptograph...
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Background: Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face re...
The classical direct product theorem for circuits says that if a Boolean function f : {0, 1}n → {0, 1} is somewhat hard to compute on average by small circuits, then the correspo...
Russell Impagliazzo, Ragesh Jaiswal, Valentine Kab...
We directly lower bound the information capacity for channels with i.i.d. deletions and duplications. Our approach differs from previous work in that we focus on the information ca...