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We advocate for a practical Maximum Likelihood Estimation (MLE) approach towards designing loss functions for regression and forecasting, as an alternative to the typical approach of direct empirical risk minimization on a specific target metric.
Note on the consistency of the maximum likelihood estimate
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On an optimal asymptotic property of the maximum likelihood estimator of a parameter from a stochastic process
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Zhi-Dong Bai and Yong-Qua Yin · 2008
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Maximum likelihood estimation of a log-concave density and its distribution function: Basic properties and uniform consistency
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Gaussian process optimization in the bandit setting: No regret and experimental design
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Random design analysis of ridge regression
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A multi-horizon quantile recurrent forecaster
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On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators
Noureddine El Karoui · 2018
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Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski · 2019
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Predicting co and no x emissions from gas turbines: novel data and a benchmark pems
Heysem Kaya, PINAR TÜFEKCİ, and Erdinc Uzun · 2019
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Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Philippe Rigollet · 2015
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Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda, and Radu Horaud · 2019
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Generalized linear models
Peter McCullagh and John A Nelder · 2019
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N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2019
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Sub-exponential concentration
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Rajat Sen, Hsiang-Fu Yu, and Inderjit Dhillon · 2019
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A modern maximum-likelihood theory for high-dimensional logistic regression
Pragya Sur and Emmanuel J Candès · 2019
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Deep factors for forecasting
Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster, and Tim Januschowski · 2019
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M5 forecasting dataset
M5 · 2020
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Robust estimation via robust gradient estimation
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Deepar: Probabilistic forecasting with autoregressive recurrent networks
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Connecting the dots: Multivariate time series forecasting with graph neural networks
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang · 2020
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Efficient first-order contextual bandits: Prediction, allocation, and triangular discrimination
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