Oliver Thomas — accepted AI-conference papers

2026

Mapping the Efficiency Landscape of Small Language Models

Fabian Reichwald, Lukas Schiesser, Christiane Plociennik, Leonhard Kunz, Simon Pukrop, Martin Ruskowski +1

IJCAI 2026

Large language models (LLMs) dominate both everyday and specialized applications, but their high computational demand, energy consumption, and privacy risks are increasingly critiqued. Small language models (SLMs) mitigate these drawbacks and are gaining momentum in scenarios where full LLM capabili

Cited by 0 Scholar ↗

2020

Null-sampling for Interpretable and Fair Representations

Thomas Kehrenberg, Myles Bartlett, Oliver Thomas, Novi Quadrianto

ECCV 2020poster

We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness. Invariance implies a selectivity for high level, relevant correlations w.r.t. class label annotations, and a robustness to irrelevant correlations with protected characteristics su…

Cited by 33 Source ↗PDF ↗code ↗Scholar ↗

2019

Discovering Fair Representations in the Data Domain

Novi Quadrianto, Viktoriia Sharmanska, Oliver Thomas

CVPR 2019poster

Interpretability and fairness are critical in computer vision and machine learning applications, in particular when dealing with human outcomes, e.g. inviting or not inviting for a job interview based on application materials that may include photographs. One promising direction to achieve fairness…

Cited by 179 PDF ↗code ↗Scholar ↗