Predicting the "Value at Risk" of a portfolio of stocks is of great significance in quantitative finance. We introduce a new class models, "dynamical products of ex...
We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known firstor...
John Duchi, Shai Shalev-Shwartz, Yoram Singer, Amb...
We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic nite automata...
Machine learning of limit programs (i.e., programs allowed finitely many mind changes about their legitimate outputs) for computable functions is studied. Learning of iterated lim...
Active learning [1] is a branch of Machine Learning in which the learning algorithm, instead of being directly provided with pairs of problem instances and their solutions (their l...