… of reliable classifiers suited for such situations. We formalize the notion and study properties
of reliable classifiers in the spirit of agnostic learning (… for reliable agnostic learning under …

… In this article, a multi-layer federated learning protocol called … of end devices (whose reliability
is agnostic). We demonstrate … experiments with machine learning tasks in different scales …

… model-agnostic method to evaluate the relative reliability of … to as the Laplacian distributed
decay for reliability (LADDR), … is used to calculate a prediction's relative reliability. LADDR is …

… model-agnostic method to evaluate the relative reliability of … to as the Laplacian distributed
decay for reliability (LADDR), … is used to calculate a prediction's relative reliability. LADDR is …

… MACEst, a Model Agnostic Confidence Estimator, which provides reliable and trustworthy …
, they learn a global representation of uncertainty and are therefore often only actually learning …

We present an agnostic active learning algorithm for any hypothesis class of bounded VC
dimension under arbitrary data distributions. Most previ-ous work on active learning either …

… The fundamental idea is to train a machine learning model by generating data from the known
model and using it as a surrogate to the known model; this accelerates the overall process …

… We study the problem of agnostic PAC reinforcement learning … state and action space) are
required to learn an $\epsilon $-… of samples to learn. This reveals a surprising separation for …

… lies in their lack of high reliability and scalability. Therefore, … cloud architectures, which is
reliable and scalable. This … Modular Machine Learning (LE2ML), a new machine learning …

… agnostic interpretation methods provides a feasible direction for solving these complex problems.
ML models provide high performance and reliability, and model-agnostic … Such reliable …