五行打油诗
发表于 2025-3-26 22:06:51
Machine Learning and Knowledge Discovery in DatabasesEuropean Conference,
Itinerant
发表于 2025-3-27 03:28:08
DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups prove that these approximate CIs are nested along specialization of patterns. This allows to incorporate pruning properties in DEvIANT to quickly discard non-significant patterns. Empirical study on several datasets demonstrates the efficiency and the usefulness of DEvIANT.
institute
发表于 2025-3-27 08:48:39
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aggrieve
发表于 2025-3-27 11:22:04
Sets of Robust Rules, and How to Find Themte-of-the-art, . does reliably recover the ground truth. On real world data we show it finds reasonable numbers of rules, that upon close inspection give clear insight in the local distribution of the data.
导师
发表于 2025-3-27 15:49:15
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resuscitation
发表于 2025-3-27 18:04:41
: Catching Hierarchical Dense Subtensorlt and select the optimal hierarchical dense subtensors. Extensive experiments on synthetic and real-world datasets demonstrate that . outperforms the top competitors in accuracy for detecting dense subtensors and anomaly patterns. Additionally, . identified a hierarchical researcher co-authorship g
精美食品
发表于 2025-3-28 02:01:37
Black Box Explanation by Learning Image Exemplars in the Latent Feature SpaceWe present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.
superfluous
发表于 2025-3-28 03:19:55
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Canvas
发表于 2025-3-28 08:50:30
Conference proceedings 2020ing and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track...Chapter "Heavy-tailed Kernels Reveal a Fine
Blazon
发表于 2025-3-28 13:46:30
DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groupsata featuring individuals (e.g., parliamentarians, customers) performing observable actions (e.g., votes, ratings) on entities (e.g., legislative procedures, movies). To this end, we introduce the problem of discovering statistically significant exceptional contextual intra-group agreement patterns.