Rchr
J-GLOBAL ID:201701008049737949   Update date: Jun. 16, 2021

Holland Matthew J.

マシュー ホーランド | Holland Matthew J.
Affiliation and department:
Job title: Assistant Professor
Research keywords  (2): Statistical learning theory ,  Machine learning
Research theme for competitive and other funds  (6):
  • 2020 - 2023 Learning with guarantees under more diverse notions of risk
  • 2019 - 2022 Robust and efficient learning algorithms through control of margin distributions
  • 2019 - 2020 Stabilization and performance guarantees for machine learning methods via margin distribution control
  • 2018 - 2019 Robust and efficient learning algorithms through control of margin distributions
  • 2017 - 2019 Safe AI is efficient AI: improved generalization via robust learning algorithms
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Papers (18):
  • Matthew J. Holland. Making learning more transparent using conformalized performance prediction. ICML 2021, Workshop on Distribution-Free Uncertainty Quantification. 2021
  • Matthew J. Holland. Robust learning with anytime-guaranteed feedback. arXiv:2105.11135 (preprint). 2021
  • Matthew J. Holland, El Mehdi Haress. Spectral risk-based learning using unbounded losses. arXiv:2105.04816 (preprint). 2021
  • Matthew J. Holland. Robustness and scalability under heavy tails, without strong convexity. Proceedings of Machine Learning Research (AISTATS 2021). 2021. 130. 1
  • Matthew J. Holland. Learning with risks based on M-location. arXiv:2012.02424v2 (preprint). 2021
more...
MISC (2):
  • Matthew J. Holland. 5分で分かる!? 有名論文ナナメ読み:Duchi, J. et al. : Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. IPSJ Magazine. 2019. 60. 8. 780-781
  • Matthew J. Holland. Robust gradient descent via back-propagation: A Chainer-based tutorial. 2019
Works (7):
  • spectral: learning based on spectral risks
    2021 - 現在
  • mrisk: learning with risks based on M-location
    Matthew J. Holland 2021 -
  • sgd-roboost: robust confidence boosting of SGD sub-processes
    Matthew J. Holland 2020 -
  • 1dim (Python): estimators using smoothed random perturbations
    Matthew J. Holland 2019 -
  • rgd (Python): Robust gradient descent examples
    Matthew J. Holland 2019 -
more...
Professional career (1):
  • Ph.D (Nara Institute of Science and Technology)
Awards (3):
  • 2018/03 - Foundation for NAIST NAIST Top Student Award (PhD Program)
  • 2017/02 - IEEE Kansai Section Student Paper Award
  • 2015/03 - Foundation for NAIST NAIST Top Student Award (Master's Program)
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