Matthew J. Holland

Publications

Here is a list of my work-related publications and pre-prints.


2020-2021

Non-monotone risk functions for learning.
Matthew J. Holland.
Preprint.
[arXiv]

Learning with risk-averse feedback under potentially heavy tails.
Matthew J. Holland and El Mehdi Haress.
Presented at: AISTATS 2021. Proceedings: PMLR 130:892-900, 2021.
[code, pdf]

Robustness and scalability under heavy tails, without strong convexity.
Matthew J. Holland.
Presented at: AISTATS 2021. Proceedings: PMLR 130:865-873, 2021.
[code, pdf]

Scaling-Up Robust Gradient Descent Techniques.
Matthew J. Holland.
Presented at: AAAI 2021. Proceedings: AAAI Digital Library Conference Proceedings, to appear.
[code]

Better scalability under potentially heavy-tailed feedback.
Matthew J. Holland.
Archival version.
[arXiv, code]

Making learning more transparent using conformalized performance prediction.
Matthew J. Holland.
Preprint.
[arXiv]


2018-2019

PAC-Bayes under potentially heavy tails.
Matthew J. Holland.
Presented at: NeurIPS 2019.
Proceedings: Advances in Neural Information Processing Systems 32, 2020.

[arXiv, Proceedings, pdf, bib]

Distribution-robust mean estimation via smoothed random perturbations.
Matthew J. Holland.
Preprint.
[arXiv, code, bib]

Better generalization with less data using robust gradient descent.
Matthew J. Holland and Kazushi Ikeda.
Presented at: ICML 2019. Proceedings: PMLR 97:2761-2770, 2019.
[PMLR, pdf, bib]

Robust gradient descent via back-propagation: A Chainer-based tutorial.
Matthew J. Holland.
[pdf, code]

Efficient learning with robust gradient descent.
Matthew J. Holland and Kazushi Ikeda.
Machine Learning, 108(8):1523-1560, 2019.
[arXiv, doi, bib]

Robust descent using smoothed multiplicative noise.
Matthew J. Holland.
Presented at: AISTATS 2019. Proceedings: PMLR 89:703-711, 2019.
[arXiv, PMLR, pdf, bib]

Classification using margin pursuit.
Matthew J. Holland.
Presented at: AISTATS 2019. Proceedings: PMLR 89:712-720, 2019.
[arXiv, code, demo, PMLR, pdf, bib]


2014-2017

Robust regression using biased objectives.
Matthew J. Holland and Kazushi Ikeda.
Journal: Machine Learning, 106(9):1643-1679, 2017.
Oral: ECML-PKDD 2017, Skopje, Macedonia.

[pdf, code, doi, bib]

Minimum proper loss estimators for parametric models.
Matthew J. Holland and Kazushi Ikeda.
IEEE Transactions on Signal Processing, 64(3):704-713, 2016.
[pdf, data, doi, bib]

Location robust estimation of predictive Weibull parameters in short-term wind speed forecasting.
Matthew J. Holland and Kazushi Ikeda.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015.
Brisbane, Australia

[doi, bib]

Forecasting in wind energy applications with site-adaptive Weibull estimation.
Matthew J. Holland and Kazushi Ikeda.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.
Florence, Italy

[doi, bib]