Geometry, Structure, and Reliable Machine Learning
Collection
Selected research on discovering structure in data, measuring data and representation geometry, and improving the reliability of machine-learning eval • 3 items • Updated
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Marios Koulakis
koulakis
·
AI & ML interests
I work on scientific machine learning, data and representation geometry, manifold learning, dimensionality reduction, generalization, and model reliability, developing controlled benchmarks and open-source tools for understanding data and learning systems.
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updated a collection about 18 hours ago
Geometry, Structure, and Reliable Machine Learning updated a collection about 18 hours ago
Geometry, Structure, and Reliable Machine Learning updated a collection about 18 hours ago
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updated a
collection about 18 hours ago
commented a
paper 26 days ago
commented a
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commented a
paper 28 days ago
submitted a
paper to Daily Papers 29 days ago
authored 6
papers 29 days ago
Domain-independent detection of known anomalies
Paper • 2407.02910 • Published
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction
Paper • 2203.12997 • Published
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?
Paper • 2405.02678 • Published
The Data Manifold under the Microscope
Paper • 2606.15760 • Published • 2
Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates
Paper • 2204.04674 • Published
Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning
Paper • 2111.15271 • Published