What are some strategies for communicating the limitations and potential failure modes of machine learning models to non-technical users?

Nov 27, 2020 ... Overfitting and underfitting are constant and ubiquitous dangers in machine learning. The goal of supervised learning is to approximate or fit a ... Any risk of informational harm to data subjects and/or organization(s) ... overfitting the training data. Regulation. Rules or laws defined and enforced ... Nov 21, 2024 ... An Information-Theoretic Perspective on Overfitting and Underfitting ... We present an information-theoretic framework for understanding ... (This is joint work with Jerry Zhu, Chuck Dyer, and Jake Rosin in Computer Sciences.) Oct. 30 Michael Titelbaum, Department of Philosophy. Why Bayesianism? Over ... Jul 27, 2024 ... ... informational impact these orders have, varying across stock ... Overfitting, ML, data snooping... And papers writing instead of ... In other words, AIC deals with both the risk of overfitting and the risk of underfitting. The Akaike information criterion is named after the Japanese ... ... informational content embodied in the data and it avoids both underfitting and overfitting of experimental observations, thus guaranteeing the principle of ... Sep 28, 2017 ... Theory of Combining VIX and Trend · Trend Following in its simplest form comes down to using information (asset price history) from the market to ... Contrary to standard rationality, investors cling stubbornly to diverse beliefs with little convergence over time. There is also empirical evidence that overall ... May 28, 2021 ... However, the CDS market has an informational advantage over the equity market which has increased with time, something not pre- viously ...