88 citations · 165 across the 6 of their papers we have counts for
6 papers
Domain Adaptation Principal Component Analysis: base linear method for learning with out-of-distribution data
Evgeny M Mirkes, Jonathan Bac, Aziz Fouché +3
Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets…
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation
Qinghua Zhou, Alexander N. Gorban, Evgeny M. Mirkes +3
Finding best architectures of learning machines, such as deep neural networks, is a well-known technical and theoretical challenge. Recent work by Mellor et al (2021) showed that t…
Scikit-dimension: a Python package for intrinsic dimension estimation
Jonathan Bac, Evgeny M. Mirkes, Alexander N. Gorban +2
Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been s…
Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data
Sergey E. Golovenkin, Jonathan Bac, Alexander Chervov +5
Large observational clinical datasets become increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considere…
Local intrinsic dimensionality estimators based on concentration of measure
Jonathan Bac, Andrei Zinovyev
Intrinsic dimensionality (ID) is one of the most fundamental characteristics of multi-dimensional data point clouds. Knowing ID is crucial to choose the appropriate machine learnin…
Estimating the effective dimension of large biological datasets using Fisher separability analysis
Luca Albergante, Jonathan Bac, Andrei Zinovyev
Modern large-scale datasets are frequently said to be high-dimensional. However, their data point clouds frequently possess structures, significantly decreasing their intrinsic dim…