An Optimal Elimination Algorithm for Learning a Best Arm
Authors: Avinatan Hassidim, Ron Kupfer, Yaron Singer
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments To illustrate the efficiency of the algorithms we conducted a simple numerical experiment. A reasonable concern may be that while our results suggest a dramatic improvement over the sample complexity of MEDIAN ELIMINATION this improvement may only be due to tighter analysis. In this section we rule out this possibility by experimentally comparing the actual sample complexity (not analysis) of our algorithms (SABA, ABA and ABALEH) with MEDIAN ELIMINATION and NAÏVE ELIMINATION. |
| Researcher Affiliation | Collaboration | Avinatan Hassidim Bar-Ilan University and Google avinatan@cs.bi.ac.il Ron Kupfer The Hebrew University of Jerudalem ron.kupfer@mail.huji.ac.il Yaron Singer Harvard University yaron@seas.harvard.edu |
| Pseudocode | Yes | Algorithm 1 NAÏVE ELIMINATION input ϵ, δ > 0, arms A, noisy oracle for µ : A [0, 1] output arm in A with largest empirical mean with 2... |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating the release of its source code. |
| Open Datasets | No | The paper does not provide concrete access information for a publicly available or open dataset. It refers to a multi-armed bandit setting with arms generating random variables but no named public dataset is mentioned for experiments. |
| Dataset Splits | No | The paper describes numerical experiments but does not provide specific details on dataset split information (e.g., percentages, sample counts for training, validation, or test sets). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper mentions conducting numerical experiments but does not provide specific experimental setup details such as concrete hyperparameter values or training configurations. |