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Optimal Decision Trees For Interpretable Clustering with Constraints (Extended Version)
In this work, we present a novel SAT-based framework for interpretable clustering that supports clustering constraints and that also provides strong theoretical guarantees on solution quality.
Ising-based Consensus Clustering on Specialized Hardware
The emergence of specialized optimization hardware such as CMOS annealers and adiabatic quantum computers carries the promise of solving hard combinatorial optimization problems more efficiently in hardware.
(Unconstrained) Beam Search is Sensitive to Large Search Discrepancies
We find that increasing the beam width leads to sequences that are disproportionately based on early and highly non-greedy decisions.
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