A modern introduction to online learning
F Orabona - arxiv preprint arxiv:1912.13213, 2019 - arxiv.org
In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …
of Online Convex Optimization. Here, online learning refers to the framework of regret …
Tuning-free maximum likelihood training of latent variable models via coin betting
We introduce two new particle-based algorithms for learning latent variable models via
marginal maximum likelihood estimation, including one which is entirely tuning-free. Our …
marginal maximum likelihood estimation, including one which is entirely tuning-free. Our …
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
In recent years, interest in gradient-based optimization over Riemannian manifolds has
surged. However, a significant challenge lies in the reliance on hyperparameters, especially …
surged. However, a significant challenge lies in the reliance on hyperparameters, especially …
Mirror and Preconditioned Gradient Descent in Wasserstein Space
As the problem of minimizing functionals on the Wasserstein space encompasses many
applications in machine learning, different optimization algorithms on $\mathbb {R}^ d …
applications in machine learning, different optimization algorithms on $\mathbb {R}^ d …
Constrained Sampling with Primal-Dual Langevin Monte Carlo
This work considers the problem of sampling from a probability distribution known up to a
normalization constant while satisfying a set of statistical constraints specified by the …
normalization constant while satisfying a set of statistical constraints specified by the …