by instadatahelp | Sep 5, 2023 | AI Blogs
Selective Inference for K-means Clustering Yiqun T. Chen, Daniela M. Witten; 24(152):1−41, 2023. Abstract We examine the issue of testing for a difference in means between clusters of observations identified through k-means clustering. Traditional hypothesis tests in...
by instadatahelp | Sep 5, 2023 | AI Blogs
Reinforcement Learning for Joint Optimization of Multiple Rewards Mridul Agarwal, Vaneet Aggarwal; 24(49):1−41, 2023. Abstract To find optimal policies that maximize the long-term rewards of Markov Decision Processes, dynamic programming and backward induction are...
by instadatahelp | Sep 5, 2023 | AI Blogs
Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process Cheng Zeng, Jeffrey W Miller, Leo L Duan; 24(153):1−32, 2023. Abstract In applications of mixture modeling and clustering, it is often unknown how many components and clusters exist....
by instadatahelp | Sep 5, 2023 | AI Blogs
Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks Lingjun Li, Jun Li; 24(51):1−44, 2023. Abstract This paper presents a novel approach for online change-point detection in high-dimensional data, specifically...
by instadatahelp | Sep 5, 2023 | AI Blogs
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd; 24(154):1−48, 2023. Abstract Analyzing the influence of changes to the training data on model predictions can provide valuable...
