Jack J. Mayo
Researcher in Machine Learning Theory

Here you can find an ordered list of my publications to date, as well as preprints:

Preprints:

Publications:

Online Newton Method for Bandit Convex Optimisation; H. Fokkema, D. v. d. Hoeven, T. Lattimore, JM*.  Proceedings of the Thirty Seventh Conference on Learning Theory, PMLR 247:1713-1714, 2024.

First-and Second-Order Bounds for Adversarial Linear Contextual Bandits; J. Olkhovskaya, JM, T. van Erven, G. Neu, C-Y Wei. Accepted to NeurIPS 2023.

Scale-free Unconstrained Online Learning for Curved LossesJM, H. Hadiji, T. van Erven. Proceedings of the Thirty Fifth Conference on Learning Theory, PMLR 178:4464-4497 (2022)

Distribution of kinks in an Ising ferromagnet after annealing and the generalized Kibble-Zurek mechanism;
JM, Z. Fan, G-W. Chern, and A. del Campo. Phys. Rev. Research 3, 033150 (2021)

Unraveling intra-aggregate structural disorder using single-molecule spectroscopy; T. Kunsel,  A. Löhner, JM,  J. Köhler,  T. L. C. Jansen, and  J. Knoester. J. Chem. Phys. 153, 134304 (2020) 

Full Counting Statistics of Topological Defects after Crossing a Phase Transition; F. J. Gómez-Ruiz, JM, and A. del Campo. Phys. Rev. Lett. 124, 240602 (2020)

* alphabetical