Portrait de Sadhana Anand

Sadhana Anand

sadhana.anand@mila.quebec
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Gauthier Gidel
Sujets de recherche
Optimisation
Théorie de l'apprentissage automatique
Théorie des jeux
Site web

Publications

Surrogate Loss Gradient Descent in Variational Inequalities with Linear Approximation
Ryan D'Orazio
Sadhana Anand
Ioannis Mitliagkas
Gauthier Gidel
The use of surrogate losses to approximate principled updates in prediction space has been instrumental in the success of scaling function a… (voir plus)pproximation in reinforcement learning. These methods are motivated by approximating an update in prediction space with an optimization inner-loop that takes a fixed number of gradient steps on a surrogate loss. Despite its prevalence, there is a disconnect between existing theory and algorithms used in practice with respect to the number of gradient steps. In this paper, we focus on the linear approximation case to derive sharp guarantees when parameters are updated by a fixed number of gradient steps on a surrogate loss. It provides a unified approach that naturally connects the mean-path analysis of TD and LSPE, which correspond to both extremes from one step to an infinite number of inner-steps, and have been previously analysed with different techniques. Our analysis, however, gives a precise insight into the compute-optimal number of gradient steps
2026-09-30
OPT @ Neural Information Processing Systems (poster)
openreview.net
openreview.net