14 citations · 15 across the 3 of their papers we have counts for
5 papers
Selecting Models based on the Risk of Damage Caused by Adversarial Attacks
Jona Klemenc, Holger Trittenbach
Regulation, legal liabilities, and societal concerns challenge the adoption of AI in safety and security-critical applications. One of the key concerns is that adversaries can caus…
Explaining Any ML Model? -- On Goals and Capabilities of XAI
Moritz Renftle, Holger Trittenbach, Michael Poznic +1
An increasing ubiquity of machine learning (ML) motivates research on algorithms to explain ML models and their predictions -- so-called eXplainable Artificial Intelligence (XAI).…
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Adrian Englhardt, Holger Trittenbach, Daniel Kottke +2
Support Vector Data Description (SVDD) is a popular one-class classifiers for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size.…
Active Learning of SVDD Hyperparameter Values
Holger Trittenbach, Klemens Böhm, Ira Assent
Support Vector Data Description is a popular method for outlier detection. However, its usefulness largely depends on selecting good hyperparameter values -- a difficult problem th…
Connecting Sharpe ratio and Student t-statistic, and beyond
Eric Benhamou
Sharpe ratio is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the excess return over the strategy standard deviation.…