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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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
| 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. Γcole 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. |