Continual learning, or lifelong learning, is a formidable current challenge to machine
learning. It requires the learner to solve a sequence of k different learning tasks, one after …

The classical PAC sample complexity bounds are stated for any Empirical Risk Minimizer
(ERM) and contain an extra logarithmic factor $\log (1/\epsilon) $ which is known to be …

We consider an active learning setting where a learner is presented with a pool $ S $ of $ n
$ unlabeled examples belonging to a domain $\mathcal X $ and asks queries to find the …

Metalearning and multitask learning are two frameworks for solving a group of related
learning tasks more efficiently than we could hope to solve each of the individual tasks on …

We study a variant of online multiclass classification where the learner predicts a single
label but receives a\textit {set of labels} as feedback. In this model, the learner is penalized …

We consider the following problem in this paper: given a set of n distributions, find the top-m
ones with the largest means. This problem is also called top-m arm identifications in the …

We consider the communication complexity of a number of distributed optimization
problems. We start with the problem of solving a linear system. Suppose there is a …

Consider the task of learning a hypothesis class $\mathcal {H} $ in the presence of an
adversary that can replace up to an $\eta $ fraction of the examples in the training set with …

We show that fundamental learning tasks, such as finding an approximate linear separator
or linear regression, require memory at least\emph {quadratic} in the dimension, in a natural …

(Abridged) Carmosino et al.(2016) demonstrated that natural proofs of circuit lower bounds
for\Lambda imply efficient algorithms for learning\Lambda-circuits, but only over the uniform …