Projection-Free Algorithm for Stochastic Bi-level Optimization

no code implementations • 22 Oct 2021 • Zeeshan Akhtar, Amrit Singh Bedi, Srujan Teja Thomdapu, Ketan Rajawat

The proposed $\textbf{S}$tochastic $\textbf{C}$ompositional $\textbf{F}$rank-$\textbf{W}$olfe ($\textbf{SCFW}$) is shown to achieve a sample complexity of $\mathcal{O}(\epsilon^{-2})$ for convex objectives and $\mathcal{O}(\epsilon^{-3})$ for non-convex objectives, at par with the state-of-the-art sample complexities for projection-free algorithms solving single-level problems.

Denoising Matrix Completion +1

Stochastic Compositional Gradient Descent under Compositional Constraints

no code implementations • 17 Dec 2020 • Srujan Teja Thomdapu, Harshvardhan, Ketan Rajawat

Of particular interest is the large-scale setting where an oracle provides the stochastic gradients of the constituent functions, and the goal is to solve the problem with a minimal number of calls to the oracle.

General Classification regression +1