Adaptive integration of molecular dynamics. - Illia Horenko; Martin Weiser (J. Comput. Chem., 2003)

Adaptive approach for nonlinear sensitivity analysis of reaction kinetics. - Illia Horenko; Sönke Lorenz; Christof Schütte; Wilhelm Huisinga (J. Comput. Chem., 2005)

Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models. - Illia Horenko; Johannes Schmidt-Ehrenberg; Christof Schütte (CompLife, 2006)

Adaptive Approach for Modelling Variability in Pharmacokinetics. - Andrea Y. Weiße; Illia Horenko; Wilhelm Huisinga (CompLife, 2006)

Automated Model Reduction for Complex Systems Exhibiting Metastability. - Illia Horenko; Evelyn Dittmer; Alexander Fischer; Christof Schütte (Multiscale Model. Simul., 2006)

Identification of biomolecular conformations from incomplete torsion angle observations by hidden markov models. - Alexander Fischer; Sonja Waldhausen; Illia Horenko; Eike Meerbach; Christof Schütte (J. Comput. Chem., 2007)

Automated Generation of Reduced Stochastic Weather Models I: Simultaneous Dimension and Model Reduction for Time Series Analysis. - Illia Horenko; Rupert Klein; Stamen Dolaptchiev; Christof Schütte (Multiscale Model. Simul., 2008)

Likelihood-Based Estimation of Multidimensional Langevin Models and Its Application to Biomolecular Dynamics. - Illia Horenko; Christof Schütte (Multiscale Model. Simul., 2008)

Macroscopic Dynamics of Complex Metastable Systems: Theory, Algorithms, and Application to B-DNA. - Illia Horenko; Evelyn Dittmer; Filip Lankas; John Maddocks; Philipp Metzner; Christof Schütte (SIAM J. Appl. Dyn. Syst., 2008)

On Metastable Conformational Analysis of Nonequilibrium Biomolecular Time Series. - Illia Horenko; Christof Schütte (Multiscale Model. Simul., 2009)

Optimal Allocation of a Futures Portfolio Utilizing Numerical Market Phase Detection. - Lars Putzig; Dirk Becherer; Illia Horenko (SIAM J. Financial Math., 2010)

Finite Element Approach to Clustering of Multidimensional Time Series. - Illia Horenko (SIAM J. Sci. Comput., 2010)

On Analysis of Nonstationary Categorical Data Time Series: Dynamical Dimension Reduction, Model Selection, and Applications To Computational Sociology. - Illia Horenko (Multiscale Model. Simul., 2011)

An Adaptive Markov Chain Monte Carlo Approach to Time Series Clustering of Processes with Regime Transition Behavior. - Jana de Wiljes; Andrew Majda; Illia Horenko (Multiscale Model. Simul., 2013)

Statistical Regression Analysis of Threshold Excesses with Systematically Missing Covariates. - Olga Kaiser; Dimitri Igdalov; Illia Horenko (Multiscale Model. Simul., 2015)

Towards a Computationally Tractable Maximum Entropy Principle for Nonstationary Financial Time Series. - Ganna Marchenko; Patrick Gagliardini; Illia Horenko (SIAM J. Financial Math., 2018)

On a scalable entropic breaching of the overfitting barrier in machine learning. - Illia Horenko (CoRR, 2020)

On a computationally-scalable sparse formulation of the multidimensional and non-stationary maximum entropy principle. - Illia Horenko; Ganna Marchenko; Patrick Gagliardini (CoRR, 2020)

On a Scalable Entropic Breaching of the Overfitting Barrier for Small Data Problems in Machine Learning. - Illia Horenko (Neural Comput., 2020)

Robust learning of data anomalies with analytically-solvable entropic outlier sparsification. - Illia Horenko (CoRR, 2021)

Co-Inference of Data Mislabelings Reveals Improved Models in Genomics and Breast Cancer Diagnostics. - Susanne Gerber; Lukás Pospísil; Stanislav Sys; Charlotte Hewel; Ali Torkamani; Illia Horenko (Frontiers Artif. Intell., 2021)

Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography. - Illia Horenko; Lukás Pospísil; Edoardo Vecchi; Steffen Albrecht; Alexander Gerber; Beate Rehbock; Albrecht Stroh; Susanne Gerber (J. Imaging, 2022)

eSPA+: Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems. - Edoardo Vecchi; Lukás Pospísil; Steffen Albrecht; Terence J. O'Kane; Illia Horenko (Neural Comput., 2022)

Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction. - Illia Horenko; Lukás Pospísil (CoRR, 2023)

On existence, uniqueness and scalability of adversarial robustness measures for AI classifiers. - Illia Horenko (CoRR, 2023)

Gauge-optimal approximate learning for small data classification problems. - Edoardo Vecchi; Davide Bassetti; Fabio Graziato; Lukás Pospísil; Illia Horenko (CoRR, 2023)

Towards Generalized Entropic Sparsification for Convolutional Neural Networks. - Tin Barisin; Illia Horenko (CoRR, 2024)

Structure-exploiting interior-point solver for high-dimensional entropy-sparsified regression learning. - Edoardo Vecchi; Juraj Kardos; Malik Lechekhab; Andreas Wächter; Illia Horenko; Olaf Schenk (J. Comput. Sci., 2024)

Gauge-Optimal Approximate Learning for Small Data Classification. - Edoardo Vecchi; Davide Bassetti; Fabio Graziato; Lukás Pospísil; Illia Horenko (Neural Comput., 2024)

On Entropic Learning from Noisy Time Series in the Small Data Regime. - Davide Bassetti; Lukás Pospísil; Illia Horenko (Entropy, 2024)

On entropic sparsification of neural networks. - Tin Barisin; Illia Horenko (Pattern Recognit. Lett., 2025)

Fast and close Shannon entropy approximation. - Illia Horenko; Davide Bassetti; Lukás Pospísil (CoRR, 2025)

An entropy-optimal path to humble AI. - Davide Bassetti; Lukás Pospísil; Michael Groom; Terence J. O'Kane; Illia Horenko (CoRR, 2025)