Papers by Daniel Cieślak
Leakage-Aware User-Level ADHD Signal Classification from Social Media: When Graph Aggregation Helps, and When It Does Not (2026.acl-srw)
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| Challenge: | Social media data are longitudinal, usercentered, rich in spontaneous language use. |
| Approach: | They propose a leakage-aware evaluation framework organized around two controlled axes: evidence budget and leakage control. |
| Outcome: | The proposed framework compares graph aggregation with other models using psycholinguistic features and semantic tweet embeddings. |
Does Locality Cost in Polish Medical Text Classification? Duplicate-Aware Evaluation of Federated Learning (2026.acl-srw)
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| Challenge: | a study of Polish medical text classification shows federated learning is a practical trade-off . centralized training is often framed as a governance concession rather than a genuinely competitive learning protocol. |
| Approach: | a study of Polish medical text classification shows federated learning is a practical trade-off . authors argue that centralized training is generally superior to centralized learning . they also argue that the results are biased by the granularity of evaluations . |
| Outcome: | a new study compares federated and centralized training in a duplicate-heavy medical text benchmark in Poland . a similar study shows that federation outperforms centralized learning in the strongest setting . |