Forecasting in wind energy applications with site-adaptive Weibull estimation. - Matthew J. Holland; Kazushi Ikeda (ICASSP, 2014)

Location robust estimation of predictive Weibull parameters in short-term wind speed forecasting. - Matthew J. Holland; Kazushi Ikeda (ICASSP, 2015)

Minimum Proper Loss Estimators for Parametric Models. - Matthew J. Holland; Kazushi Ikeda (IEEE Trans. Signal Process., 2016)

Robust regression using biased objectives. - Matthew J. Holland; Kazushi Ikeda (Mach. Learn., 2017)

Robust descent using smoothed multiplicative noise. - Matthew J. Holland (AISTATS, 2019)

Classification using margin pursuit. - Matthew J. Holland (AISTATS, 2019)

Better generalization with less data using robust gradient descent. - Matthew J. Holland; Kazushi Ikeda (ICML, 2019)

PAC-Bayes under potentially heavy tails. - Matthew J. Holland (NeurIPS, 2019)

Efficient learning with robust gradient descent. - Matthew J. Holland; Kazushi Ikeda (Mach. Learn., 2019)

Learning with CVaR-based feedback under potentially heavy tails. - Matthew J. Holland; El Mehdi Haress (CoRR, 2020)

Making learning more transparent using conformalized performance prediction. - Matthew J. Holland (CoRR, 2020)

Non-monotone risk functions for learning. - Matthew J. Holland (CoRR, 2020)

Better scalability under potentially heavy-tailed feedback. - Matthew J. Holland (CoRR, 2020)

Scaling-Up Robust Gradient Descent Techniques. - Matthew J. Holland (AAAI, 2021)

Learning with risk-averse feedback under potentially heavy tails. - Matthew J. Holland; El Mehdi Haress (AISTATS, 2021)

Robust learning with anytime-guaranteed feedback. - Matthew J. Holland (CoRR, 2021)

Designing off-sample performance metrics. - Matthew J. Holland (CoRR, 2021)

Anytime Guarantees under Heavy-Tailed Data. - Matthew J. Holland (AAAI, 2022)

Spectral risk-based learning using unbounded losses. - Matthew J. Holland; El Mehdi Haress (AISTATS, 2022)

Risk regularization through bidirectional dispersion. - Matthew J. Holland (CoRR, 2022)

Learning with risks based on M-location. - Matthew J. Holland (Mach. Learn., 2022)

Flexible risk design using bi-directional dispersion. - Matthew J. Holland (AISTATS, 2023)

Implicit regularization via soft ascent-descent. - Matthew J. Holland; Kosuke Nakatani (CoRR, 2023)

A Survey of Learning Criteria Going Beyond the Usual Risk. - Matthew J. Holland; Kazuki Tanabe (J. Artif. Intell. Res., 2023)

A Survey of Learning Criteria Going beyond the Usual Risk (Abstract Reprint). - Matthew J. Holland; Kazuki Tanabe (AAAI, 2024)

Robust variance-regularized risk minimization with concomitant scaling. - Matthew J. Holland (AISTATS, 2024)

Criterion Collapse and Loss Distribution Control. - Matthew J. Holland (ICML, 2024)

Soft ascent-descent as a stable and flexible alternative to flooding. - Matthew J. Holland; Kosuke Nakatani (NeurIPS, 2024)

Making Robust Generalizers Less Rigid with Soft Ascent-Descent. - Matthew J. Holland; Toma Hamada (CoRR, 2024)