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ORCIDMatthew J. Holland
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2020 – today
- 2024
[c14]Matthew J. Holland, Kazuki Tanabe:
A Survey of Learning Criteria Going beyond the Usual Risk (Abstract Reprint). AAAI 2024: 22698- [c13]Matthew J. Holland:
Robust variance-regularized risk minimization with concomitant scaling. AISTATS 2024: 1144-1152 - [c12]Matthew J. Holland:
Criterion Collapse and Loss Distribution Control. ICML 2024 - [i15]Matthew J. Holland:
Criterion collapse and loss distribution control. CoRR abs/2402.09802 (2024) - [i14]Matthew J. Holland, Toma Hamada:
Making Robust Generalizers Less Rigid with Soft Ascent-Descent. CoRR abs/2408.03619 (2024) - 2023
- [j5]Matthew J. Holland, Kazuki Tanabe:
A Survey of Learning Criteria Going Beyond the Usual Risk. J. Artif. Intell. Res. 78: 781-821 (2023) - [c11]Matthew J. Holland:
Flexible risk design using bi-directional dispersion. AISTATS 2023: 1586-1623 - [i13]Matthew J. Holland:
Robust variance-regularized risk minimization with concomitant scaling. CoRR abs/2301.11584 (2023) - [i12]Matthew J. Holland, Kosuke Nakatani:
Implicit regularization via soft ascent-descent. CoRR abs/2310.10006 (2023) - 2022
- [j4]Matthew J. Holland
:
Learning with risks based on M-location. Mach. Learn. 111(12): 4679-4718 (2022) - [c10]Matthew J. Holland:
Anytime Guarantees under Heavy-Tailed Data. AAAI 2022: 6918-6925 - [c9]Matthew J. Holland, El Mehdi Haress:
Spectral risk-based learning using unbounded losses. AISTATS 2022: 1871-1886 - [i11]Matthew J. Holland:
Risk regularization through bidirectional dispersion. CoRR abs/2203.14434 (2022) - 2021
- [c8]Matthew J. Holland:
Scaling-Up Robust Gradient Descent Techniques. AAAI 2021: 7694-7701 - [c7]Matthew J. Holland, El Mehdi Haress:
Learning with risk-averse feedback under potentially heavy tails. AISTATS 2021: 892-900 - [i10]Matthew J. Holland, El Mehdi Haress:
Spectral risk-based learning using unbounded losses. CoRR abs/2105.04816 (2021) - [i9]Matthew J. Holland:
Robust learning with anytime-guaranteed feedback. CoRR abs/2105.11135 (2021) - [i8]Matthew J. Holland:
Designing off-sample performance metrics. CoRR abs/2110.04996 (2021) - 2020
- [i7]Matthew J. Holland, El Mehdi Haress:
Learning with CVaR-based feedback under potentially heavy tails. CoRR abs/2006.02001 (2020) - [i6]Matthew J. Holland:
Making learning more transparent using conformalized performance prediction. CoRR abs/2007.04486 (2020) - [i5]Matthew J. Holland:
Non-monotone risk functions for learning. CoRR abs/2012.02424 (2020) - [i4]Matthew J. Holland:
Better scalability under potentially heavy-tailed feedback. CoRR abs/2012.07346 (2020)
2010 – 2019
- 2019
- [j3]Matthew J. Holland, Kazushi Ikeda:
Efficient learning with robust gradient descent. Mach. Learn. 108(8-9): 1523-1560 (2019) - [c6]Matthew J. Holland:
Robust descent using smoothed multiplicative noise. AISTATS 2019: 703-711 - [c5]Matthew J. Holland:
Classification using margin pursuit. AISTATS 2019: 712-720 - [c4]Matthew J. Holland, Kazushi Ikeda:
Better generalization with less data using robust gradient descent. ICML 2019: 2761-2770 - [c3]Matthew J. Holland:
PAC-Bayes under potentially heavy tails. NeurIPS 2019: 2711-2720 - [i3]Matthew J. Holland:
PAC-Bayes under potentially heavy tails. CoRR abs/1905.07900 (2019) - 2018
- [i2]Matthew J. Holland:
Classification using margin pursuit. CoRR abs/1810.04863 (2018) - [i1]Matthew J. Holland:
Robust descent using smoothed multiplicative noise. CoRR abs/1810.06207 (2018) - 2017
- [j2]Matthew J. Holland, Kazushi Ikeda:
Robust regression using biased objectives. Mach. Learn. 106(9-10): 1643-1679 (2017) - 2016
- [j1]Matthew J. Holland, Kazushi Ikeda:
Minimum Proper Loss Estimators for Parametric Models. IEEE Trans. Signal Process. 64(3): 704-713 (2016) - 2015
- [c2]Matthew J. Holland, Kazushi Ikeda:
Location robust estimation of predictive Weibull parameters in short-term wind speed forecasting. ICASSP 2015: 3996-4000 - 2014
- [c1]Matthew J. Holland, Kazushi Ikeda:
Forecasting in wind energy applications with site-adaptive Weibull estimation. ICASSP 2014: 2184-2188
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