WebJul 6, 2024 · Coreset-based Learning: As shown in Figure 3 (b), the coreset degenerates into the entire training dataset to achieve full accuracy. The focus is thus on the partial accuracy scenario, where we set the coreset size to 1,500, containing 100 random samples (considered part of the exposed training data S t r a i n t a r g e t ) and 700 local ... WebJun 30, 2024 · In this paper, we propose a novel robust coreset method for the {\em continuous-and-bounded learning} problems (with outliers) which includes a broad range of popular optimization objectives in machine learning, {\em e.g.,} logistic regression and -means clustering.
Robust Coreset for Continuous-and-Bounded Learning (with …
WebHowever, a robust, end-to-end training approach, like adversarial training, is yet to be discovered for backdoor poisoned data. In this paper, we take the first step toward such methods by developing a robust training framework, COLLIDER, that selects the most prominent samples by exploiting the underlying geometric structures of the data. Webcoreset approximates the model learned on the original dataset. However, existing coreset construction algorithms are tailor-made for specific machine learning problems, which … ship\\u0027s w7
A survey of methods for distributed machine learning - Semantic …
WebThe existing robust coreset construction methods [19, 26] often rely on simple uniform sampling and are efficient only when the number of outliers is a constant factor of the input size (we will discuss this issue in Section 3.1). Note that other outlier-resistant data summary methods WebJan 13, 2024 · After proving the submodularity and monotonicity of our coverage function, the Robust-Coreset algorithm is provided to compute a small coreset of dataset D that sufficiently approximates D. Then, a k -coverage query algorithm Robust-Threshold takes the coreset as input and can return a solution set with approximately the largest coverage … WebJun 30, 2024 · Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems. In many machine learning tasks, a common approach for … ship\\u0027s wake definition