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cs.LG2026
On Hyperparameter Tuning on the Test Set
Matteo Fregonara, Tom Viering, Jan van Gemert
"Don't tune hyperparameters on the test set" is often stated in machine learning textbooks. Violating it is considered a cardinal sin that produces misleadingly optimistic results,…
cs.CR2026
MalTree: Tracing Malware Evolution from Embeddings at Scale
Akash Amalan, Georgios Smaragdakis, Tom J. Viering
Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware famil…
cs.LG2026
-PFN: Fast Entropy Search via In-Context Learning
Herilalaina Rakotoarison, Steven Adriaensen, Tom Viering +4
Information-theoretic acquisition functions such as Entropy Search (ES) offer a principled exploration-exploitation framework for Bayesian optimization (BO). However, their practic…