activity
20172026
most citedUncovering Temporal Patterns in Visualizations of High-Dimensional Data

2 citations · 3 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SE2026

Is manual software optimization a thing of the past?

Pavlin G. Poličar, Martin Špendl, Tomaž Hočevar

Scientific software is increasingly required to process larger datasets while maintaining acceptable execution times. Software optimization traditionally requires substantial exper…

cs.LG2026

Generating Statistical Charts with Validation-Driven LLM Workflows

Pavlin G. Poličar, Andraž Pevcin, Blaž Zupan

Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are not detectable from data or…

cs.LG2026

Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach

Pavlin G. Poličar, Dalibor Stanimirović, Blaž Zupan

Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records incomplete and unreliable for determining disease onset. In contrast, outpatient…

cs.LG2025

Automated Assignment Grading with Large Language Models: Insights From a Bioinformatics Course

Pavlin G. Poličar, Martin Špendl, Tomaž Curk +1

Providing students with individualized feedback through assignments is a cornerstone of education that supports their learning and development. Studies have shown that timely, high…

cs.LG2024

VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation

Pavlin G. Poličar, Blaž Zupan

Two-dimensional embeddings obtained from dimensionality reduction techniques such as MDS, t-SNE, or UMAP, are widely used to visualize high-dimensional data and support researchers…

cs.LG2024★ 2 cited

Uncovering Temporal Patterns in Visualizations of High-Dimensional Data

Pavlin G. Poličar, Blaž Zupan

With the increasing availability of high-dimensional data, analysts often rely on exploratory data analysis to understand complex data sets. A key approach to exploring such data i…

cs.SI2017★ 1 cited

Data clustering using stochastic block models

Nina Mrzelj, Pavlin Gregor Poličar

It has been shown that community detection algorithms work better for clustering tasks than other, more popular methods, such as k-means. In fact, network analysis based methods of…