Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Ulf Brefeld,Elisa Fromont,Céline Robardet Conference proceeding

厭食癥

易于出錯(cuò)

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

兵團(tuán)

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

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—

反應(yīng)

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

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

商店街

生存環(huán)境

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

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.