Matthew J. Holland

35 papers A* 7A 6Misc 2Journal 20

Year	Rank	Type	Title / Venue / Authors
2024	A*	`conf`	A Survey of Learning Criteria Going beyond the Usual Risk (Abstract Reprint). AAAI Matthew J. Holland, Kazuki Tanabe
2024	A*	`conf`	Criterion Collapse and Loss Distribution Control. ICML Matthew J. Holland
2024	J	`jnl`	Criterion collapse and loss distribution control. CoRR Matthew J. Holland
2024	J	`jnl`	Making Robust Generalizers Less Rigid with Soft Ascent-Descent. CoRR Matthew J. Holland, Toma Hamada
2024	A	`conf`	Robust variance-regularized risk minimization with concomitant scaling. AISTATS Matthew J. Holland
2024	A*	`conf`	Soft ascent-descent as a stable and flexible alternative to flooding. NeurIPS Matthew J. Holland, Kosuke Nakatani
2023	J	`jnl`	A Survey of Learning Criteria Going Beyond the Usual Risk. J. Artif. Intell. Res. Matthew J. Holland, Kazuki Tanabe
2023	A	`conf`	Flexible risk design using bi-directional dispersion. AISTATS Matthew J. Holland
2023	J	`jnl`	Implicit regularization via soft ascent-descent. CoRR Matthew J. Holland, Kosuke Nakatani
2023	J	`jnl`	Robust variance-regularized risk minimization with concomitant scaling. CoRR Matthew J. Holland
2022	A*	`conf`	Anytime Guarantees under Heavy-Tailed Data. AAAI Matthew J. Holland
2022	J	`jnl`	Learning with risks based on M-location. Mach. Learn. Matthew J. Holland
2022	J	`jnl`	Risk regularization through bidirectional dispersion. CoRR Matthew J. Holland
2022	A	`conf`	Spectral risk-based learning using unbounded losses. AISTATS Matthew J. Holland, El Mehdi Haress
2021	J	`jnl`	Designing off-sample performance metrics. CoRR Matthew J. Holland
2021	A	`conf`	Learning with risk-averse feedback under potentially heavy tails. AISTATS Matthew J. Holland, El Mehdi Haress
2021	J	`jnl`	Robust learning with anytime-guaranteed feedback. CoRR Matthew J. Holland
2021	A*	`conf`	Scaling-Up Robust Gradient Descent Techniques. AAAI Matthew J. Holland
2021	J	`jnl`	Spectral risk-based learning using unbounded losses. CoRR Matthew J. Holland, El Mehdi Haress
2020	J	`jnl`	Better scalability under potentially heavy-tailed feedback. CoRR Matthew J. Holland
2020	J	`jnl`	Learning with CVaR-based feedback under potentially heavy tails. CoRR Matthew J. Holland, El Mehdi Haress
2020	J	`jnl`	Making learning more transparent using conformalized performance prediction. CoRR Matthew J. Holland
2020	J	`jnl`	Non-monotone risk functions for learning. CoRR Matthew J. Holland
2019	A*	`conf`	Better generalization with less data using robust gradient descent. ICML Matthew J. Holland, Kazushi Ikeda
2019	A	`conf`	Classification using margin pursuit. AISTATS Matthew J. Holland
2019	J	`jnl`	Efficient learning with robust gradient descent. Mach. Learn. Matthew J. Holland, Kazushi Ikeda
2019	A*	`conf`	PAC-Bayes under potentially heavy tails. NeurIPS Matthew J. Holland
2019	J	`jnl`	PAC-Bayes under potentially heavy tails. CoRR Matthew J. Holland
2019	A	`conf`	Robust descent using smoothed multiplicative noise. AISTATS Matthew J. Holland
2018	J	`jnl`	Classification using margin pursuit. CoRR Matthew J. Holland
2018	J	`jnl`	Robust descent using smoothed multiplicative noise. CoRR Matthew J. Holland
2017	J	`jnl`	Robust regression using biased objectives. Mach. Learn. Matthew J. Holland, Kazushi Ikeda
2016	J	`jnl`	Minimum Proper Loss Estimators for Parametric Models. IEEE Trans. Signal Process. Matthew J. Holland, Kazushi Ikeda
2015	Misc	`conf`	Location robust estimation of predictive Weibull parameters in short-term wind speed forecasting. ICASSP Matthew J. Holland, Kazushi Ikeda
2014	Misc	`conf`	Forecasting in wind energy applications with site-adaptive Weibull estimation. ICASSP Matthew J. Holland, Kazushi Ikeda