Optimistic Bandit Convex Optimization
Authors: Scott Yang, Mehryar Mohri
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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. |