Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.
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This paper establishes a mathematical duality framework for adversarial total variation, showing that adversarial training of binary classifiers can be understood through nonlocal calculus of variations. The work provides rigorous characterizations of subdifferentials using dual representations and integration by parts formulas in both metric and Euclidean spaces.