The method of defensive forecasting is applied to the problem of prediction with expert advice for binary outcomes. It turns out that defensive forecasting is not only competitive with the Aggregating Algorithm but also handles the case of "second-guessing" experts, whose advice depends on the learner's prediction; this paper assumes that the dependence on the learner's prediction is continuous.
Departments of Computer Science & Engineering and Data Science, New York University（纽约大学计算机科学与工程系和数据科学系）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）
AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy
London School of Economics and Political Science（伦敦政治经济学院）；Massachusetts Institute of Technology（麻省理工学院）；Federal Reserve Bank of Chicago（芝加哥联邦储备银行）；University of California San Diego（加利福尼亚大学圣迭戈分校）；University of Pennsylvania（宾夕法尼亚大学）
Adaptive Selective Sampling for Online Prediction with Experts
Eindhoven University of Technology（埃因霍温理工大学）；Eindhoven Artificial Intelligence Systems Institute (EAISI)（埃因霍温人工智能系统研究所（EAISI））；University College London（伦敦大学学院）；University of Amsterdam（阿姆斯特丹大学）
