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cs.LG2025
Gradient Prediction with Control Variates in the Cheap-Forward Regime
Kamil Ciosek, Nicolò Felicioni, Juan Elenter +1
We study whether otherwise-idle inference resources could reduce the scarce-GPU cost of training. Our analysis uses a simulated compute ledger in which fleet work is billed at a fr…
cs.LG2025
Feasible Learning
Juan Ramirez, Ignacio Hounie, Juan Elenter +4
We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. In…