Johannes Fürnkranz

Globally optimized Boolean rule sets are now achievable with explicit control over accuracy-sparsity trade-offs, thanks to a novel MILP formulation that drastically cuts down on integer variables.

RePAIR reveals that self-supervised learning can produce semantically rich representations of chess positions, enabling intuitive game analysis without reinforcement learning.