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..
Fast Rates for Bandit PAC Multiclass Classification
Authors: Liad Erez, Alon Peled-Cohen, Tomer Koren, Yishay Mansour, Shay Moran
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is in designing a novel learning algorithm for the agnostic (e, δ)-PAC version of the problem, with sample complexity of O (poly(K) + 1/e2) log(|H|/δ) for any finite hypothesis class H. |
| Researcher Affiliation | Collaboration | Liad Erez Tel-Aviv University EMAIL Alon Cohen Tel-Aviv University Google Research EMAIL Tomer Koren Tel-Aviv University Google Research EMAIL Yishay Mansour Tel-Aviv University Google Research EMAIL Shay Moran Technion Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Bandit PAC Multiclass Classification via Log Barrier Stochastic Optimization, Algorithm 2 Stochastic Frank-Wolfe with SPIDER gradient estimates |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a link to a code repository. The NeurIPS checklist indicates it is a theoretical paper. |
| Open Datasets | No | The paper is theoretical and does not use external public datasets for training. It describes an internal process of constructing a dataset 'S' within the algorithm, but this is not a publicly available dataset in the typical sense. |
| Dataset Splits | No | The paper is theoretical and does not describe training, validation, or test dataset splits for empirical experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an empirical experimental setup, such as hyperparameters or training configurations. |