Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
Authors: Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work considers the full information and bandit feedback models. Complete proofs of all theorems may be found in the appendices. |
| Researcher Affiliation | Collaboration | Yuval Filmus Faculty of Computer Science Faculty of Mathematics Technion, Israel EMAIL Steve Hanneke Department of Computer Science Purdue University, USA EMAIL Idan Mehalel Faculty of Computer Science Technion, Israel EMAIL Faculty of Mathematics Faculty of Computer Science Faculty of Data and Decision Sciences Technion, Israel Google research, Israel EMAIL |
| Pseudocode | Yes | Figure 1: Bandit Rand SOA ... Figure 2: The doubling trick" algorithm DT. |
| Open Source Code | No | The paper is theoretical and focuses on mathematical proofs and algorithm design. It does not contain any statements about releasing open-source code for its described methodologies, nor does it provide links to such repositories. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets. Therefore, it does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore it does not provide specific dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments; thus, it does not specify hardware used. |
| Software Dependencies | No | The paper is theoretical and does not report on experimental implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |