Jan 26, 2022 · In our work, we seek to solve the problem at its source, collecting only valuable data and throwing out the rest, via active learning. We ...
We propose an online algorithm which, given any stream of data, any assessment of its value, and any formulation of its selection cost, extracts the most ...
Finally, in learning tasks on ImageNet and MNIST, we show that our selection methods outperform random selection by 5-20\%. Subjects: Machine ...
Jan 25, 2022 · A survey on online active learning ... Online active learning is a paradigm in machine learning that aims to se... 0 Davide Cacciarelli, et al. ∙.
Online Active Learning with Dynamic Marginal Gain Thresholding. 2022. A. N. Angelopoulos; S. Bates; E. J. Candès; M. I. Jordan; L. Lei. Learn then Test ...
Online Active Learning with Dynamic Marginal Gain Thresholding [2022]; Online ... An Online Active Broad Learning Approach for Real-Time Safety Assessment of ...
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Jan 25, 2022 · 我们提出了一种在线算法，给定任何数据流、对其价值的任何评估以及其选择成本的任何公式，在使用最少内存的同时提取流中最有价值的子集直到一个常数因子。
arXiv preprint, 2022. [arXiv] [code] [bibtex]. “Online Active Learning with Dynamic Marginal Gain Thresholding”. M. Werner, A. Angelopoulos, S. Bates, and ...
In this thesis we address online decision making problems where an agent needs to collect optimal training data to fit statistical models.
Our bound gives a guarantee for any choice of threshold schedule. Tables 1 and 2 illustrate a few examples of many different possible pairings between practical ...