Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
Authors: Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran
NeurIPS 2024 | Conference 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 filmus.yuval@gmail.com Steve Hanneke Department of Computer Science Purdue University, USA steve.hanneke@gmail.com Idan Mehalel Faculty of Computer Science Technion, Israel idanmehalel@gmail.com Faculty of Mathematics Faculty of Computer Science Faculty of Data and Decision Sciences Technion, Israel Google research, Israel shaymoran1@gmail.com |
| 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. |