May 18, 2022 · We develop a framework for constructing uncertainty sets that provably control risk -- such as coverage of confidence intervals, false negative rate, or F1 ...
Oct 24, 2023 · The paper proposes a practical method for controlling user-defined risks in online settings. While the theoretical contributions are considered ...
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This work develops a framework for constructing uncertainty sets that provably control risk -- such as coverage of confidence intervals, false negative rate ...
Feb 1, 2023 · TL;DR: A flexible tool for constructing uncertainty estimates with a rigorous long-range risk control (such as coverage, false negative rate, or ...
Bates, and Y. Romano, Achieving Risk Control in Online Learning Settings, Transactions on Machine Learning Research (TMLR), 2023. Code. G.
Rolling RC is a method that reliably reports the uncertainty of a target variable response in an online time-series setting and provably attains the user- ...
Revisit your plans regularly to reassess the effectiveness of your risk management controls and make necessary changes accordingly.
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Learn then test: Calibrating predictive algorithms to achieve risk control ... Achieving risk control in online learning settings. S Feldman, L Ringel, S ...
To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk ...
“Achieving Risk Control in Online Learning Settings”. S. Feldman, L. Ringel, S. Bates, and Y. Romano. Transactions on Machine Learning Research, 2023. [arXiv] ...