We develop algorithms for online linear regression which achieve optimal static and
dynamic regret guarantees\emph {even in the complete absence of prior knowledge}. We …

A sequence of works in unconstrained online convex optimisation have investigated the
possibility of adapting simultaneously to the norm U of the comparator and the maximum …

We consider the problem of online linear regression in the stochastic setting. We derive high
probability regret bounds for online $\textit {ridge} $ regression and the $\textit {forward} …

Motivated by the EU's" Right To Be Forgotten" regulation, we initiate a study of statistical
data deletion problems where users' data are accessible only for a limited period of time …

We study fixed-design online learning where the learner is allowed to choose the order of
the datapoints in order to minimize their regret (aka self-directed online learning). We focus …

Endogeneity, ie the dependence between noise and covariates, is a common phenomenon
in real data due to omitted variables, strategic behaviours, measurement errors etc. In …

The aim of this paper is to design computationally-efficient and optimal algorithms for the
online and stochastic exp-concave optimization settings. Typical algorithms for these …

This paper proposes a three-step approach to forecasting time series of electricity
consumption at different levels of household aggregation. These series are linked by …

Acquiring an accurate world model online for model-based reinforcement learning (MBRL)
is challenging due to data nonstationarity, which typically causes catastrophic forgetting for …

We are interested in a framework of online learning with kernels for low-dimensional, but
large-scale and potentially adversarial datasets. We study the computational and theoretical …