What are some ongoing research areas related to understanding and mitigating overfitting in machine learning?

(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 ... We introduce an importance-weighted entropy-based measure to counter the tendency of common binning approaches to over-emphasise information contained in the ... Nov 21, 2024 ... An Information-Theoretic Perspective on Overfitting and Underfitting ... We present an information-theoretic framework for understanding ... Jun 26, 2019 ... “We emphasise intelligence and imagination over “fitting in.” We ... We are funny first, and informative second, hosting the party you want to ... Nov 7, 2024 ... The application of principles derived from research on causality has benefited current learning models by constraining the search over the space ... According to Eli Pariser, those algorithms create “a unique universe of information for each of us … ... over another, competing candidates would have no way of ... : Entropy aids in determining the most informative features for splitting the data, thereby maximizing information gain. ... data without overfitting to the noise ... Experiment 4: Drawing More Attention to the Fact that Over-Informative Information is Not Helpful. In Experiment 3, and in previous research (Engelhardt et ... Therefore, we have chosen a parametric approach with low dimensionality over. © Planet Labs PBC. 2024 - Proprietary and Confidential. FOR INFORMATIONAL PURPOSES ... Jul 26, 2023 ... By addressing issues such as overfitting, data quality, path ... The opinions and ideas presented herein are for informational and ...