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Predicting sets of outcomes -- instead of unique outcomes -- is a promising solution to uncertainty quantification in statistical learning.
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Boosting Algorithms as Gradient Descent
Mason, L., J. Baxter, P. L. Bartlett, and M. Frean (2000) · 2000
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A Generally Efficient Targeted Minimum Loss Based Estimator based on the Highly Adaptive Lasso
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Double/debiased machine learning for treatment and structural parameters
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Distribution-Free Predictive Inference for Regression
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Robust variance estimation and inference for causal effect estimation
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Targeted learning in data science: causal inference for complex longitudinal studies
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Van der Laan, M. J. and S. Rose (2018) · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
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Statistical analysis with missing data
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Calibrated Model-Based Deep Reinforcement Learning
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Least Ambiguous Set-Valued Classifiers With Bounded Error Levels
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Conformal prediction under covariate shift
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Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
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Universal sieve-based strategies for efficient estimation using machine learning tools
Qiu, H., A. Luedtke, and M. Carone (2021) · 2021
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Distribution-free, risk-controlling prediction sets
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Semiparametric doubly robust targeted double machine learning: a review
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Doubly Robust Calibration of Prediction Sets under Covariate Shift
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Variance estimation for the average treatment effects on the treated and on the controls
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