3 citations · 8 across the 9 of their papers we have counts for
10 papers
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…
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…
A benchmark of categorical encoders for binary classification
Federico Matteucci, Vadim Arzamasov, Klemens Boehm
Categorical encoders transform categorical features into numerical representations that are indispensable for a wide range of machine learning models. Existing encoder benchmark st…
Adaptive Bernstein Change Detector for High-Dimensional Data Streams
Marco Heyden, Edouard Fouché, Vadim Arzamasov +3
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., b…
Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals
Marco Heyden, Vadim Arzamasov, Edouard Fouché +1
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from arms with unknown expected rewards and costs. The goal is to maximize the total r…
Pedagogical Rule Extraction to Learn Interpretable Models - an Empirical Study
Vadim Arzamasov, Benjamin Jochum, Klemens Böhm
Machine-learning models are ubiquitous. In some domains, for instance, in medicine, the models' predictions must be interpretable. Decision trees, classification rules, and subgrou…
REDS: Rule Extraction for Discovering Scenarios
Vadim Arzamasov, Klemens Böhm
Scenario discovery is the process of finding areas of interest, known as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions, i.e.,…