WebOn the Hardness of Robust Classification The present paper is about robust learnability, and important problem for our ML community. The authors provide both theoretical and methodological contributions to address sample complexity and computational efficiency in the robust learning framework.

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WebOn the Hardness of Robust Classification. Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell. Year: 2024, Volume: 22, Issue: 273, Pages: 1−29. … Web2 de out. de 2024 · This work proves that, for a broad set of classification tasks, the mere existence of a robust classifier implies that it can be found by a possibly exponential-time algorithm with relatively few training examples and gives an exponential separation between classical learning and robust learning in the statistical query model. asussystemanalysis是什么意思

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WebFinally, we provide a simple proof of the computational hardness of robust learning on the boolean hypercube. Unlike previous results of this nature, our result does not rely on another computational model (e.g. the statistical query model) nor on any hardness assumption other than the existence of a hard learning problem in the PAC framework. WebFinally, we provide a simple proof of the computational hardness of robust learning on the boolean hypercube. Unlike previous results of this nature, our result does not rely on another computational model (e.g. the statistical query model) nor on any hardness assumption other than the existence of a hard learning problem in the PAC framework. WebThese associations are robust to a number of confounding variables in multivariate logistic and time to event analyses. Furthermore, the time to event analysis controlling for squamous cell carcinoma diagnosis led to a statistically significant association between woody hardness (i.e., A/B higher risk) and time to stricture (HR=5, p=0.02). asussaio