Publications
Here is a list including most research-related publications and preprints written by me and my colleagues.
Soft ascent-descent as a stable and flexible alternative to flooding
Matthew J. Holland and Kosuke Nakatani
Presented at NeurIPS 2024, Vancouver, Canada.
Advances in Neural Information Processing Systems 37, 2024.
[proceedings, arXiv, code]
Making Robust Generalizers Less Rigid with Soft Ascent-Descent
Matthew J. Holland and Toma Hamada
Preprint.
[arXiv, code]
Criterion Collapse and Loss Distribution Control
Matthew J. Holland
Presented at ICML 2024, Vienna, Austria.
Proceedings of Machine Learning Research 235:18547-18567, 2024.
[proceedings, arXiv, code]
Robust variance-regularized risk minimization with concomitant scaling
Matthew J. Holland
Presented at AISTATS 2024, Valencia, Spain.
Proceedings of Machine Learning Research 238:1144-1152, 2024.
[proceedings, arXiv, code]
A Survey of Learning Criteria Going Beyond the Usual Risk
Matthew J. Holland and Kazuki Tanabe
Journal: Journal of Artificial Intelligence Research, 78:781-821, 2023.
Oral: AAAI 2024 (Journal track), Vancouver, Canada.
[journal, doi, arXiv]
Flexible risk design using bi-directional dispersion
Matthew J. Holland
Presented at AISTATS 2023, Valencia, Spain.
Proceedings of Machine Learning Research 206:1586-1623, 2023.
[proceedings, arXiv, code]
Learning with risks based on M-location
Matthew J. Holland
Journal: Machine Learning, 111:4679-4718, 2022.
Oral: ECML-PKDD 2022, Grenoble, France.
[journal, doi, arXiv, code]
Spectral risk-based learning using unbounded losses
Matthew J. Holland and El Mehdi Haress
Presented at AISTATS 2022, online.
Proceedings of Machine Learning Research 151:1871-1886, 2022.
[proceedings, arXiv, code]
Anytime Guarantees under Heavy-Tailed Data
Matthew J. Holland
Presented at AAAI 2022, online.
Proceedings of the AAAI Conference on Artificial Intelligence, 36(6):6918-6925.
[proceedings, doi, arXiv, code]
Making learning more transparent using conformalized performance prediction
Matthew J. Holland
Presented at ICML 2021, Workshop on Distribution-Free Uncertainty Quantification.
[arXiv]
Learning with risk-averse feedback under potentially heavy tails
Matthew J. Holland and El Mehdi Haress
Presented at AISTATS 2021, online.
Proceedings of Machine Learning Research 130:892-900, 2021.
[proceedings, arXiv, code]
Robustness and scalability under heavy tails, without strong convexity
Matthew J. Holland
Presented at AISTATS 2021, online.
Proceedings of Machine Learning Research 130:865-873, 2021.
[proceedings, code]
Scaling-Up Robust Gradient Descent Techniques
Matthew J. Holland
Presented at AAAI 2021, online.
Proceedings of the AAAI Conference on Artificial Intelligence, 35(9):7694-7701.
[proceedings, doi, code]
Better scalability under potentially heavy-tailed feedback
Matthew J. Holland
Archival version.
[arXiv, code]
PAC-Bayes under potentially heavy tails
Matthew J. Holland
Presented at NeurIPS 2019, Vancouver, Canada.
Advances in Neural Information Processing Systems 32, 2019.
[proceedings, arXiv]
Distribution-robust mean estimation via smoothed random perturbations
Matthew J. Holland
Preprint.
[arXiv, code]
Better generalization with less data using robust gradient descent
Matthew J. Holland and Kazushi Ikeda
Presented at ICML 2019, Long Beach, USA.
Proceedings of Machine Learning Research 97:2761-2770, 2019.
[proceedings]
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
Journal: Machine Learning, 108(8):1523-1560, 2019.
Oral: ECML-PKDD 2019, Wurzburg, Germany.
[journal, doi, arXiv]
Robust descent using smoothed multiplicative noise
Matthew J. Holland
Presented at AISTATS 2019, Naha, Japan.
Proceedings of Machine Learning Research 89:703-711, 2019.
[proceedings]
Classification using margin pursuit
Matthew J. Holland
Presented at AISTATS 2019, Naha, Japan.
Proceedings of Machine Learning Research 89:712-720, 2019.
[proceedings, code]
Robust regression using biased objectives
Matthew J. Holland and Kazushi Ikeda
Journal: Machine Learning, 106(9):1643-1679, 2017.
Oral: ECML-PKDD 2017, Skopje, North Macedonia.
[journal, doi, code]
Minimum proper loss estimators for parametric models
Matthew J. Holland and Kazushi Ikeda
IEEE Transactions on Signal Processing, 64(3):704-713, 2016.
[doi]
Location robust estimation of predictive Weibull parameters in short-term wind speed forecasting
Matthew J. Holland and Kazushi Ikeda
ICASSP 2015, Brisbane, Australia.
[doi]
Forecasting in wind energy applications with site-adaptive Weibull estimation
Matthew J. Holland and Kazushi Ikeda
ICASSP 2014, Florence, Italy.
[doi]
