Tom Viering — accepted AI-conference papers

2026

$\alpha$-PFN: Fast Entropy Search via In-Context Learning

Herilalaina Rakotoarison, Steven Adriaensen, Tom Viering, Samuel Gabriel Müller, Carl Hvarfner, Frank Hutter +1

ICML 2026poster

Information-theoretic acquisition functions such as Entropy Search (ES) offer a principled exploration–exploitation framework for Bayesian optimization (BO). However, their practical implementation relies on complicated and slow approximations, i.e., a Monte Carlo estimation of the information gain.…

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2026

MalTree: Tracing Malware Evolution using Embeddings at Scale

Akash Amalan, Georgios Smaragdakis, Tom Viering

ICML 2026poster

Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware families can inform proactive defense, but traditional reverse engineering can take months to years to uncover such lineage r…

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2019

Minimizers of the Empirical Risk and Risk Monotonicity

Marco Loog, Tom Viering, Alexander Mey

NeurIPS 2019poster

Plotting a learner's average performance against the number of training samples results in a learning curve. Studying such curves on one or more data sets is a way to get to a better understanding of the generalization properties of this learner. The behavior of learning curves is, however, not ve…

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