35 citations · 99 across the 13 of their papers we have counts for
9 papers · 1 filter
Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration
Sabin Roman, Ljupco Todorovski, Saso Dzeroski
We develop the Sparse Orthogonal Regression Technique (SORT), a sparse spectral framework for learning orthonormal-basis expansions from noisy and irregularly sampled data. SORT es…
Limits of spectral learning under noise
Sabin Roman, Ljupco Todorovski, Saso Dzeroski +2
Learning functional relationships from noisy data is a central problem in scientific inference. Spectral methods approximate unknown functions by expanding them in a basis and esti…
Combining Textual and Structural Information for Premise Selection in Lean
Job Petrovčič, David Eliecer Narvaez Denis, Ljupčo Todorovski
Premise selection is a key bottleneck for scaling theorem proving in large formal libraries. Yet existing language-based methods often treat premises in isolation, ignoring the web…
Efficient Generator of Mathematical Expressions for Symbolic Regression
Sebastian Mežnar, Sašo Džeroski, Ljupčo Todorovski
We propose an approach to symbolic regression based on a novel variational autoencoder for generating hierarchical structures, HVAE. It combines simple atomic units with shared wei…
Explaining the Performance of Multi-label Classification Methods with Data Set Properties
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski +1
Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behaviour of machine learning…
Comprehensive Comparative Study of Multi-Label Classification Methods
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski +1
Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and…
Probabilistic Grammars for Equation Discovery
Jure Brence, Ljupčo Todorovski, Sašo Džeroski
Equation discovery, also known as symbolic regression, is a type of automated modeling that discovers scientific laws, expressed in the form of equations, from observed data and ex…
Equation Discovery for Nonlinear System Identification
Nikola Simidjievski, Ljupčo Todorovski, Juš Kocijan +1
Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method…
Meta-Model Framework for Surrogate-Based Parameter Estimation in Dynamical Systems
Žiga Lukšič, Jovan Tanevski, Sašo Džeroski +1
The central task in modeling complex dynamical systems is parameter estimation. This task involves numerous evaluations of a computationally expensive objective function. Surrogate…