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..
The Blinded Bandit: Learning with Adaptive Feedback
Authors: Ofer Dekel, Elad Hazan, Tomer Koren
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop ef๏ฌcient online learning algorithms for this problem and prove that they guarantee the same asymptotic regret as the optimal algorithms for the standard multi-armed bandit problem. In this paper, we present a new algorithm for the blinded bandit setting and prove that it guarantees a regret of O(T) on any oblivious sequence of loss values. |
| Researcher Affiliation | Collaboration | Ofer Dekel Microsoft Research EMAIL Elad Hazan Technion EMAIL Tomer Koren Technion EMAIL |
| Pseudocode | Yes | Algorithm 1: BLINDED EXP3 and Algorithm 2: BLINDED GEOMETRICHEDGE |
| Open Source Code | No | The paper does not mention providing access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |