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Found 15 papers, 8 papers with code
Efficient Generation of Hidden Outliers for Improved Outlier Detection
The only existing method accounting for this property falls short in efficiency and effectiveness.
Uncertainty-Aware Partial-Label Learning
In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels.
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.
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e. g., predictive maintenance, fraud detection, or medicine.
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.
Efficient Subspace Search in Data Streams
In the real world, data streams are ubiquitous -- think of network traffic or sensor data.
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary.
Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey
Ultimately, to guide the choice of the generation approach in a specific context, we propose an appropriate general-decision process.
Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization
Previous work has addressed this challenge by personalizing general recognition models to the unique motion pattern of a new user in a static batch setting.
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data
This allows both for a good coverage of domains and for helpful interpretations of results.
Active Learning of SVDD Hyperparameter Values
Existing methods to estimate hyperparameter values are purely heuristic, and the conditions under which they work well are unclear.
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.
Monte Carlo Dependency Estimation
In this paper, we propose Monte Carlo Dependency Estimation (MCDE), a theoretical framework to estimate multivariate dependency in static and dynamic data.
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
This article starts with a categorization of the various methods.
