Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..

Non-stationary Bandit Convex Optimization: A Comprehensive Study

Authors: Xiaoqi Liu, Dorian Baudry, Julian Zimmert, Patrick Rebeschini, Arya Akhavan

NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 36,190 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,024 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.

Reproducibility Variable Result LLM Response
Research Type Theoretical This paper is theoretical in nature and does not include experimental results.
Researcher Affiliation Collaboration University of Oxford. Correspondence to: EMAIL. Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, 38000 Grenoble, France. Google Research. Γ‰cole Polytechnique de Paris, IP Paris.
Pseudocode Yes Algorithm 1 Tilted Exponentially Weighted Average with Sleeping Experts (TEWA-SE) (...) Algorithm 2 Expert algorithm E(I, Ο±): projected online gradient descent (OGD) (...) Algorithm 3 clipped Exploration by Optimization (c Ex O) (...) Algorithm 4 TEWA equipped with Bandit-over-Bandit (Bo B)
Open Source Code No This paper does not include experiments.
Open Datasets No This paper does not include experiments.
Dataset Splits No This paper does not include experiments.
Hardware Specification No This paper does not include experiments.
Software Dependencies No This paper is theoretical in nature and does not include experimental results.
Experiment Setup No This paper does not include experiments.