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Leander Kurscheidt

2 accepted papers

2026

The Theory and Practice of MAP Inference over Non-Convex Constraints

ICML 2026poster

In many safety-critical settings, probabilistic ML systems have to make predictions subject to algebraic constraints, e.g., predicting the most likely trajectory that does not cross obstacles. These real-world constraints are rarely convex, nor the densities considered are (log-)concave. This makes …

Cited by 0SourceScholar
2025

A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction

UAI 2025

In safety-critical applications, guaranteeing the satisfaction of constraints over continuous environments is crucial, e.g., an autonomous agent should never crash over obstacles or go off-road. Neural models struggle in the presence of these constraints, especially when they involve intricate algeb