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Konstantinos Skianis

2 accepted papers

2026

On the Lipschitz Continuity of Set Aggregation Functions and Neural Networks for Sets

ICLR 2026poster

The Lipschitz constant of a neural network is connected to several important properties of the network such as its robustness and generalization. It is thus useful in many settings to estimate the Lipschitz constant of a model. Prior work has focused mainly on estimating the Lipschitz constant of mu…

Cited by 0SourceScholar
2020

Rep the Set: Neural Networks for Learning Set Representations

AISTATS 2020poster

In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventional machine learning algorithms are unable to process this kind of representations, since sets may vary in cardinality and…