2 papers
cs.LG2026
How Much Computation Does Every Forecast Need? Structured Block Skipping in Two Time-Series Foundation Models
Max Bourgeat
Time-series foundation models use the same network depth for every input, although forecasting windows differ in structure and difficulty. We test whether whole Transformer blocks…
cs.AI2024
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning
Swann Bessa, Darius Dabert, Max Bourgeat +2
Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems. This bo…