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Found 6 papers, 3 papers with code
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Data
Outlier detection in high-dimensional tabular data is an important task in data mining, essential for many downstream tasks and applications.
Efficient Generation of Hidden Outliers for Improved Outlier Detection
The only existing method accounting for this property falls short in efficiency and effectiveness.
Adaptive Bernstein Change Detector for High-Dimensional Data Streams
In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur.
Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from $K$ arms with unknown expected rewards and costs.
Pedagogical Rule Extraction to Learn Interpretable Models - an Empirical Study
Decision trees, classification rules, and subgroup discovery are three broad categories of supervised machine-learning models presenting knowledge in the form of interpretable rules.
REDS: Rule Extraction for Discovering Scenarios
Given a computational budget, results tend to get worse as the number of inputs of the simulation model and the cost of simulations increase.
