厌食症
发表于 2025-3-27 00:08:05
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易于出错
发表于 2025-3-27 05:04:06
Distributed Learning of Non-convex Linear Models with One Round of Communicationmunication, works on non-convex problems, and supports a fast cross validation procedure. The OWA algorithm first trains local models on each of the compute nodes; then a master machine merges the models using a second round of optimization. This second optimization uses only a small fraction of the
兵团
发表于 2025-3-27 05:23:57
SLSGD: Secure and Efficient Distributed On-device Machine Learningrithm with efficient communication and attack tolerance. The proposed algorithm has provable convergence and robustness under non-IID settings. Empirical results show that the proposed algorithm stabilizes the convergence and tolerates data poisoning on a small number of workers.
惹人反感
发表于 2025-3-27 10:11:02
Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learningwith minimal programming effort. This is especially true for machine learning problems whose objective function is nicely separable across individual data points, such as classification and regression. In contrast, statistical learning tasks involving pairs (or more generally tuples) of data points—
反应
发表于 2025-3-27 16:53:01
Importance Weighted Generative Networksaining. Specifically, the training data distribution may differ from the target sampling distribution due to sample selection bias, or because the training data comes from a different but related distribution. We present methods to accommodate this difference via ., which allow us to estimate a loss
MILL
发表于 2025-3-27 19:43:18
Unjustified Classification Regions and Counterfactual Explanations in Machine Learningy impacts the vulnerability of the model. Additionally, we show that state-of-the-art post-hoc counterfactual approaches can minimize the impact of this risk by generating less local explanations (Source code available at: .).
Isometric
发表于 2025-3-28 01:17:51
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商店街
发表于 2025-3-28 03:57:26
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生存环境
发表于 2025-3-28 07:27:52
Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Datale when the labeling mechanism is not fully understood and propose a practical method to enable this. Our empirical analysis supports the theoretical results and shows that taking into account the possibility of a selection bias, even when the labeling mechanism is unknown, improves the trained classifiers.
Mutter
发表于 2025-3-28 13:12:22
Cost Sensitive Evaluation of Instance Hardness in Machine Learningnd can be seen as an expected loss of difficulty along cost proportions. Different cost curves were proposed by considering common decision threshold choice methods in literature, thus providing alternative views of instance hardness.