Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits

Masoud Moravej Khorasani, Erik Weyer
2/12/2026

No confident code implementation found for this paper. 3 possible matches can be shown below.

DOISemantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Non-parametric Sensor Noise Modeling and Synthesis

96% match2026

Non-parametric Online Change Point Detection on Riemannian Manifolds

92% match1 repo72026

ADAPT: Attentive Self-Distillation and Dual-Decoder Prediction Fusion for Continual Panoptic Segmentation

79% match1 repo52026

Partition First, Embed Later: Laplacian-Based Feature Partitioning for Refined Embedding and Visualization of High-Dimensional Data

79% match1 repo32026

EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction

79% match2026

Surrogate Prompt Learning: Towards Efficient and Diverse Prompt Learning for Vision-Language Models

79% match2026

Cite this paper

@article{khorasani2026maximum,
  title  = {Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits},
  author = {Masoud Moravej Khorasani and Erik Weyer},
  year   = {2026},
  doi    = {10.52202/075280-2567},
  url    = {https://doi.org/10.52202/075280-2567},
  journal = {NEURIPS 2023 2023}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/47f21f90d0bec5a17bca8ffad845b86859f2f0f4.svg)](https://codeofpaper.com/paper/47f21f90d0bec5a17bca8ffad845b86859f2f0f4)

Discussion