← Search

Daniela Angela Parletta

2 accepted papers

2026

High-Probability Bounds for the Last Iterate of Clipped SGD

ICLR 2026poster

We study the problem of minimizing a convex objective when only noisy gradient estimates are available. Under the mild assumption that the stochastic gradients have finite $\alpha$-th moments for some $\alpha \in (1,2]$, we show that the last iterate of clipped stochastic gradient descent (Clipped-S…

Cited by 0SourceScholar
2021

Robust Unsupervised Learning via L-statistic Minimization

ICML 2021spotlight

Designing learning algorithms that are resistant to perturbations of the underlying data distribution is a problem of wide practical and theoretical importance. We present a general approach to this problem focusing on unsupervised learning. The key assumption is that the perturbing distribution is…

Cited by 14SourcePDFScholar