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cs.LG2026
Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations
Koyena Pal, Serdar Kadioglu
Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These embeddings were shown to enab…
cs.LG2025
FORGE: Foundational Optimization Representations from Graph Embeddings
Zohair Shafi, Serdar Kadioglu
Combinatorial optimization problems are ubiquitous in science and engineering. Still, learning-based approaches to accelerate combinatorial optimization often require solving a lar…
cs.LG2024
Scalable iterative pruning of large language and vision models using block coordinate descent
Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz +4
Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certa…