This paper investigates the logistic bandit problem, a variant of the generalized linear bandit
model that utilizes a logistic model to depict the feedback from an action. While most existing …

We analyze Newton's method with lazy Hessian updates for solving general possibly non-
convex optimization problems. We propose to reuse a previously seen Hessian for several …

In this paper, we study greedy variants of quasi-Newton methods. They are based on the
updating formulas from a certain subclass of the Broyden family. In particular, this subclass …

Popular machine learning estimators involve regularization parameters that can be
challenging to tune, and standard strategies rely on grid search for this task. In this paper …

Differentially private inference via noisy optimization Page 1 The Annals of Statistics 2023,
Vol. 51, No. 5, 2067–2092 https://doi.org/10.1214/23-AOS2321 © Institute of Mathematical …

The classical asymptotic theory for parametric M-estimators guarantees that, in the limit of
infinite sample size, the excess risk has a chi-square type distribution, even in the …

We introduce\emph {exploration via linear loss perturbations}(EVILL), a randomised
exploration method for structured stochastic bandit problems that works by solving for the …

Logistic bandit is a ubiquitous framework of modeling users' choices, eg, click vs. no click for
advertisement recommender system. We observe that the prior works overlook or neglect …

We show that Newton's method converges globally at a linear rate for objective functions
whose Hessians are stable. This class of problems includes many functions which are not …

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between
a discrete and a generic (possibly non-discrete) probability measure, are believed to be …