Publications
Here is a list including most research-related publications and preprints written by me and my colleagues.
Making Robust Generalizers Less Rigid with Loss Concentration
Matthew J. Holland and Toma Hamada
Presented at ICONIP 2025, Onna, Japan.
Communications in Computer and Information Science 2754:391-405, 2026.
[proceedings, doi, arXiv, code]
Learning as choosing a loss distribution
Matthew J. Holland
VISxAI 2025, Vienna, Austria.
[explainer]
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 37:37955-37987, 2024.
[proceedings, doi, 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]
