This paper notes a simple connection between synthetic control and online learning.
Specifically, we recognize synthetic control as an instance of Follow‐The‐Leader (FTL) …

Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices
Page 1 The Annals of Statistics 2022, Vol. 50, No. 4, 2157–2178 https://doi.org/10.1214/22-AOS2181 …

We study online linear regression problems in a distributed setting, where the data is spread
over a network. In each round, each network node proposes a linear predictor, with the …

This monograph provides an overview of distributed online optimization in multi-agent
systems. Online optimization approaches planning and decision problems from a robust …

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} …

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 …

We consider learning a stochastic bandit model, where the reward function belongs to a
general class of uniformly bounded functions, and the additive noise can be …

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 …

We revisit the sequential variants of linear regression with the squared loss, classification
problems with hinge loss, and logistic regression, all characterized by unbounded losses in …