# A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization

arXiv: https://arxiv.org/abs/2502.05600 · HTML: https://mlsanity.com/paper/2502.05600/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning](https://mlsanity.com/paper/2606.14970/) (2026)
- [Parameter-Free Zeroth-Order Optimization with Ellipsoidal Sampling](https://mlsanity.com/paper/2609.38561/) (2026)

## Abstract (arXiv)

This paper considers zeroth-order optimization for stochastic convex minimization problem. We propose a parameter-free stochastic zeroth-order method (POEM) by introducing a step-size scheme based on the distance over finite difference and an adaptive smoothing parameter. We provide the theoretical analysis to show that POEM achieves the near-optimal stochastic zeroth-order oracle complexity. We further conduct the numerical experiments to demonstrate POEM outperforms existing zeroth-order methods in practice.