Matthew J. Holland

Flexible risk design using bi-directional dispersion 

AISTATS 2023 (to appear), Apr, 2023  Peer-reviewed

Learning criteria going beyond the usual risk 

arXiv preprint, Dec, 2022  

Learning with risks based on M-location 

Machine Learning, 111 4679-4718, Aug, 2022  Peer-reviewed

Spectral risk-based learning using unbounded losses 

Proceedings of Machine Learning Research (AISTATS 2022), 151(1) 1871-1886, Mar, 2022  Peer-reviewed

Anytime Guarantees under Heavy-Tailed Data 

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2022), 36(6) 6918-6925, Feb, 2022  Peer-reviewed

Making learning more transparent using conformalized performance prediction 

ICML 2021, Workshop on Distribution-Free Uncertainty Quantification, Jul, 2021  Peer-reviewed

Robustness and scalability under heavy tails, without strong convexity 

Proceedings of Machine Learning Research (AISTATS 2021), 130(1), Apr, 2021  Peer-reviewed

Learning with risk-averse feedback under potentially heavy tails 

Proceedings of Machine Learning Research (AISTATS 2021), 130(1), Apr, 2021  Peer-reviewed

Scaling-Up Robust Gradient Descent Techniques 

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2021)., 35(9) 7694-7701, Feb, 2021  Peer-reviewed

Better scalability under potentially heavy-tailed feedback 

Archival version, Dec, 2020  

PAC-Bayes under potentially heavy tails 

Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Dec, 2019  Peer-reviewed

Efficient learning with robust gradient descent 

Machine Learning, 108(8) 1523-1560, Sep, 2019  Peer-reviewed

Distribution-robust mean estimation via smoothed random perturbations 

arXiv preprint, Jun, 2019  

Better generalization with less data using robust gradient descent 

Proceedings of Machine Learning Research (ICML 2019), 97 2761-2770, Jun, 2019  Peer-reviewed

Robust descent using smoothed multiplicative noise 

Proceedings of Machine Learning Research (AISTATS 2019), 89(1) 703-711, Apr, 2019  Peer-reviewed

Classification using margin pursuit 

Proceedings of Machine Learning Research (AISTATS 2019), 89(1) 712-720, Apr, 2019  Peer-reviewed

Robust regression using biased objectives 

Machine Learning, 106(9-10) 1643-1679, Oct, 2017  Peer-reviewed

Minimum Proper Loss Estimators for Parametric Models 

IEEE Transactions on Signal Processing, 64(3) 704-713, Feb, 2016  Peer-reviewed

Location robust estimation of predictive Weibull parameters in short-term wind speed forecasting 

Proceedings of ICASSP 2015, Apr, 2015  Peer-reviewed