Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indr? ?liobait? Confer

Impacted

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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track網絡公開度學科排名




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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次學科排名




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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track年度引用學科排名




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書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track讀者反饋學科排名

heckle

Reinventing Node-centric Traffic Forecasting for?Improved Accuracy and?Efficiencyg methods, we identify two primary research approaches: node-centric and graph-centric. Node-centric methods focus on constructing spatial features through preprocessing and modeling spatial correlations in the input space. In contrast, graph-centric methods mainly rely on graph neural networks to c

生命層

Direct-Effect Risk Minimization for?Domain Generalizationhis is known as the problem of correlation shift and has posed concerns on the reliability of machine learning. In this work, we introduce the framework of direct and indirect effects from causal inference to the domain generalization problem. Models that learn direct effects minimize the worst-case

paragon

Federated Frank-Wolfe Algorithmrithms for constrained machine learning problems are still limited, particularly when the projection step is costly. To this end, we propose a Federated Frank-Wolfe Algorithm (.). . features data privacy, low per-iteration cost, and communication of sparse signals. In the deterministic setting, . ac

Costume

Malleable

Deep Sketched Output Kernel Regression for?Structured Predictionwide variety of output modalities. In particular, they have been successfully used in the context of surrogate non-parametric regression, where the kernel trick is typically exploited in the input space as well. However, when inputs are images or texts, more expressive models such as deep neural net

jealousy

deriver

不近人情

ANIM

Probabilistic Circuits with?Constraints via?Convex Optimization class of tractable models that allow efficient computations (such as conditional and marginal probabilities) while achieving state-of-the-art performance in some domains. The proposed approach takes both a PC and constraints as inputs, and outputs a new PC that satisfies the constraints. This is do