In the setting of stochastic online learning with undirected feedback gr...
We consider the adversarial online multi-task reinforcement learning set...
Much of the work in online learning focuses on the study of sublinear up...
We consider two variants of private stochastic online learning. The firs...
We consider a sequential decision-making problem where an agent can take...
We study a variant of decision-theoretic online learning in which the se...
Recent work introduced loss functions which measure the error of a predi...
We present a novel notion of complexity that interpolates between and
ge...
The adaptive gradient online learning method known as AdaGrad has seen
w...
We present new excess risk bounds for general unbounded loss functions
i...
The speed with which a learning algorithm converges as it is presented w...
MLPACK is a state-of-the-art, scalable, multi-platform C++ machine learn...
Since its inception, the modus operandi of multi-task learning (MTL) has...
The goal of predictive sparse coding is to learn a representation of exa...