6 papers · 1 filter
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…
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…
Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data
Jose Cribeiro-Ramallo, Federico Matteucci, Paul Enciu +4
Outlier detection in high-dimensional tabular data is challenging since data is often distributed across multiple lower-dimensional subspaces -- a phenomenon known as the Multiple…
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…
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…
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…