A modular CPU-GPU batching framework for branch-and-bound delivers 10-100x speedups with zero optimality gap when certifying optimal cardinality-constrained GLMs.
Supersparse linear integer models for optimized medical scoring systems.Machine Learning, 102(3):349–391
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From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs
A modular CPU-GPU batching framework for branch-and-bound delivers 10-100x speedups with zero optimality gap when certifying optimal cardinality-constrained GLMs.