12 citations · 17 across the 15 of their papers we have counts for
15 papers
On a joint simultaneous learning of relevant feature subsets and subspaces in regression-like problems
Illia Horenko
We extend a recently introduced Entropy-Optimal Manifold Clustering (EOMC) to allow for a joint simultaneous identification of subsets and subspaces of relevant features in nonstat…
Inferring Magnetic Material Parameters from Statistical Measures in Strongly Fluctuating Magnetization Dynamics
Kübra Kalkan, Atreya Majumdar, Ross Knapman +5
Magnetic material parameters such as the exchange stiffness and magnetic anisotropy govern the behavior and functionality of magnetic systems, yet their local inference from magnet…
Distillation and Interpretability of Ensemble Forecasts of ENSO Phase using Entropic Learning
Michael Groom, Davide Bassetti, Illia Horenko +1
This paper introduces a distillation framework for an ensemble of entropy-optimal Sparse Probabilistic Approximation (eSPA) models, trained exclusively on satellite-era observation…
Linearly-scalable and entropy-optimal learning of nonstationary and nonlinear manifolds
Illia Horenko
We propose an Entropy-Optimal Manifold Clustering (EOMC) - and show that it mitigates the cost scaling and robustness issues of the existing dimensionality reduction and manifold l…
An entropy-optimal path to humble AI
Davide Bassetti, Lukáš Pospíšil, Michael Groom +2
Progress of AI has led to very successful, but by no means humble models and tools, especially regarding (i) the huge and further exploding costs and resources they demand, and (ii…
Fast, close, non-singular and property-preserving approximations of entropic measures
Illia Horenko, Davide Bassetti, Lukáš Pospíšil
Entropic measures like Shannon entropy (SE), its quantum mechanical analogue von Neumann entropy, and Kullback-Leibler divergence (KL) are key components in many tools used in phys…
Entropic learning enables skilful forecasts of ENSO phase at up to two years lead time
Michael Groom, Davide Bassetti, Illia Horenko +1
This paper extends previous work (Groom et al., \emph{Artif. Intell. Earth Syst.}, 2024) in applying the entropy-optimal Sparse Probabilistic Approximation (eSPA) algorithm to pred…
Towards Generalized Entropic Sparsification for Convolutional Neural Networks
Tin Barisin, Illia Horenko
Convolutional neural networks (CNNs) are reported to be overparametrized. The search for optimal (minimal) and sufficient architecture is an NP-hard problem as the hyperparameter s…
Entropic perspective on AI resilience, data-minimal learning and optimal control
Illia Horenko
Behind the general terms like adversarial attacks and generative adversarial networks (GANs), such criteria as AI resilience and AI robustness gained a crucial importance, in tryin…
Gauge-optimal approximate learning for small data classification problems
Edoardo Vecchi, Davide Bassetti, Fabio Graziato +2
Small data learning problems are characterized by a significant discrepancy between the limited amount of response variable observations and the large feature space dimension. In t…
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction
Illia Horenko, Lukas Pospisil
In many data science applications, the objective is to extract appropriately-ordered smooth low-dimensional data patterns from high-dimensional data sets. This is challenging since…
Robust learning of data anomalies with analytically-solvable entropic outlier sparsification
Illia Horenko
Entropic Outlier Sparsification (EOS) is proposed as a robust computational strategy for the detection of data anomalies in a broad class of learning methods, including the unsuper…
On Computationally-Scalable Spatio-Temporal Regression Clustering of Precipitation Threshold Excesses
Olga Kaiser, Dimitri Igdalov, Olivia Martius +1
Focusing on regression based analysis of extremes in a presence of systematically missing covariates, this work presents a data-driven spatio-temporal regression based clustering o…
On a scalable entropic breaching of the overfitting barrier in machine learning
Illia Horenko
Overfitting and treatment of "small data" are among the most challenging problems in the machine learning (ML), when a relatively small data statistics size is not enough to pr…
Physically-inspired computational tools for sharp detection of material inhomogeneities in magnetic imaging
Illia Horenko, Davi Rodrigues, Terence O'Kane +1
Detection of material inhomogeneities is an important task in magnetic imaging and plays a significant role in understanding physical processes. For example, in spintronics, the sa…