To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method.

In large-scale applications, datasets often contain billions of high-dimensional points. Grouping similar data points into clusters is crucial for understanding and organizing datasets.

In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.

Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.

However, the most common algorithm used for this problem in practical applications is a natural greedy algorithm.

This means that the algorithm designer needs to balance several conflicting desiderata.

In this paper, we initiate the study of Euclidean clustering with Distance-based privacy. Distance-based privacy is motivated by the fact that it is often only needed to protect the privacy of exact, rather than approximate, locations.

Thus, we verify that the random effects estimator is equivalent to the generative model (1). Specifically, if u(x) = 1 for all x X, we use ( X, P,ψ) for simplicity. Due to the separability of ψ, we have the following coreset definition. Definitions 2.2 and 2.3, the regression objectives of OLSE and GLSE can be decomposed into Thus, we can apply the above definition to define coresets for OLSE and GLSE. Now we are ready to describe the FL framework in the language of a query space. We first prove Theorem C.1 and propose the corresponding algorithm that constructs an Next, we prove Theorem C.2 and propose the corresponding algorithm that constructs an accurate Caratheodory's Theorem, there must exist at most To accelerate the running time, Jubran et al. [ By the Caratheodory's Theorem, there must exist at most In this section, we complete the proofs for GLSE.