Societal and real-world considerations such as robustness, fairness, social welfare and multi-
agent tradeoffs have given rise to multi-distribution learning paradigms, such as …

In this work, we aim to characterize the statistical complexity of realizable regression both in
the PAC learning setting and the online learning setting. Previous work had established the …

We investigate the adversarial robustness of streaming algorithms. In this context, an
algorithm is considered robust if its performance guarantees hold even if the stream is …

We provide a novel reduction from swap-regret minimization to external-regret minimization,
which improves upon the classical reductions of Blum-Mansour and Stoltz-Lugosi in that it …

We prove novel algorithmic guarantees for several online problems in the smoothed
analysis model. In this model, at each time step an adversary chooses an input distribution …

Given an input that undergoes a sequence of updates, a dynamic algorithm maintains a
valid solution to some predefined problem at any point in time; the goal is to design an …

While ERM suffices to attain near-optimal generalization error in the stochastic learning
setting, this is not known to be the case in the online learning setting, where algorithms for …

Robustness against adversarial attacks has recently been at the forefront of algorithmic
design for machine learning tasks. In the adversarial streaming model, an adversary gives …

We extend the classical theory of PAC learning in a way which allows to model a rich variety
of practical learning tasks where the data satisfy special properties that ease the learning …

The equivalence of realizable and agnostic learnability is a fundamental phenomenon in
learning theory. With variants ranging from classical settings like PAC learning and …