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..
Optimistic Bandit Convex Optimization
Authors: Scott Yang, Mehryar Mohri
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce the general and powerful scheme of predicting information re-use in optimization algorithms. This allows us to devise a computationally efficient algorithm for bandit convex optimization with new state-of-the-art guarantees for both Lipschitz loss functions and loss functions with Lipschitz gradients. |
| Researcher Affiliation | Collaboration | Mehryar Mohri Courant Institute and Google Scott Yang Courant Institute |
| Pseudocode | Yes | Figure 1: Pseudocode of OPTIMISTICBCO, with R: int(K) ! R, δ 2 (0, 1], > 0, k 2 Z, and x1 2 K. |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the described methodology is being released. |
| Open Datasets | No | This paper is theoretical and does not use or refer to specific datasets for training, validation, or testing. Therefore, no concrete access information for a publicly available or open dataset is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets that would require validation splits. No information about dataset splits was provided. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experiments that would require hardware specifications. No hardware details were mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not describe any experiments that would require software dependencies with version numbers. No such details were mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experiments that would involve hyperparameter tuning or specific training setups. No such details were mentioned. |