Christoforos Sparatlis

Christoforos Sparatlis

Electrical and Computer Engineer, Research Assistant, PhD Candidate. AI enthusiast dedicated to making AI models forget private or harmful information on demand. Commited to a future where AI and technology serve people, not used against them.

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portfolio

publications

LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty

Published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

We present LoTUS, a novel Machine Unlearning (MU) method that eliminates the influence of training samples from pre-trained models, avoiding retraining from scratch. LoTUS smooths the prediction probabilities of the model up to an information-theoretic bound, mitigating its over-confidence stemming from data memorization. We evaluate LoTUS on Transformer and ResNet18 models against eight baselines across five public datasets. Beyond established MU benchmarks, we evaluate unlearning on ImageNet1k, a large-scale dataset, where retraining is impractical, simulating real-world conditions. Moreover, we introduce the novel Retrain-Free Jensen-Shannon Divergence (RF-JSD) metric to enable evaluation under real-world conditions. The experimental results show that LoTUS outperforms state-of-the-art methods in terms of both efficiency and effectiveness. Code: https://github.com/cspartalis/LoTUS.

Recommended citation: Spartalis et al. (2025). "LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2025.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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