34 citations · 85 across the 11 of their papers we have counts for
15 papers
Informed Asymmetric Actor-Critic: Leveraging Privileged Signals Beyond Full-State Access
Daniel Ebi, Damien Ernst, Klemens Böhm +1
Asymmetric reinforcement learning leverages privileged information available during training to improve learning under partial observability. Existing asymmetric actor-critic metho…
Evaluating Privacy Measures for Load Hiding
Vadim Arzamasov, Klemens Böhm
In smart grids, the use of smart meters to measure electricity consumption at a household level raises privacy concerns. To address them, researchers have designed various load hid…
Generalizability of experimental studies
Federico Matteucci, Vadim Arzamasov, Jose Cribeiro-Ramallo +3
Experimental studies are a cornerstone of Machine Learning (ML) research. A common and often implicit assumption is that the study's results will generalize beyond the study itself…
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Data
Jose Cribeiro-Ramallo, Vadim Arzamasov, Federico Matteucci +2
Outlier detection in high-dimensional tabular data is an important task in data mining, essential for many downstream tasks and applications. Existing unsupervised outlier detectio…
Efficient Generation of Hidden Outliers for Improved Outlier Detection
Jose Cribeiro-Ramallo, Vadim Arzamasov, Klemens Böhm
Outlier generation is a popular technique used for solving important outlier detection tasks. Generating outliers with realistic behavior is challenging. Popular existing methods t…
Partial-Label Learning with a Reject Option
Tobias Fuchs, Florian Kalinke, Klemens Böhm
In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels. Partial-label learning allows training classi…