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..
Multi-Fidelity Best-Arm Identification
Authors: Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we numerically validate IISE, showing the benefits of our method in simulated domains. |
| Researcher Affiliation | Academia | Riccardo Poiani DEIB, Politecnico di Milano EMAIL Alberto Maria Metelli DEIB, Politecnico di Milano EMAIL Marcello Restelli DEIB, Politecnico di Milano EMAIL |
| Pseudocode | Yes | Our solution (pseudo-code in Algorithm 1 and visual representation in Figure 1) builds on the Successive Elimination algorithm [10]. |
| Open Source Code | Yes | The code for generating the synthetic bandits and the Yahtzee game is released in the supplementary material. |
| Open Datasets | Yes | More specifically, we consider the Yahtzee game [3]. |
| Dataset Splits | No | The paper describes experimental parameters but does not specify dataset splits (e.g., train/validation/test percentages or counts) in the main text. The problem context (Best-Arm Identification) does not typically involve static dataset splits for training or validation. |
| Hardware Specification | Yes | The experiments were run on a machine with an Intel Core i7-4770K CPU (3.50 GHz), 16GB of RAM, and a GeForce RTX 2080 Ti GPU (11GB). |
| Software Dependencies | Yes | The algorithms were implemented in Python 3.8 and ran on PyTorch 1.9. |
| Experiment Setup | Yes | Synthetic A setting parameters are K = 2000, M = 4, λ = [1, 10, 100, 1000], ξ = [1.15, 0.225, 0.015, 0], γ = [0.3, 0.05, 0.001, 0]; for Synthetic B, instead, K = 1000, M = 5, λ = [16, 64, 256, 1024, 4096], ξ = [1.15, 0.45, 0.105, 0.0105, 0], γ = [0.3, 0.1, 0.01, 0.001, 0]. For both the synthetic bandits and the Yahtzee game, we have chosen the parameters such that the conditions of Assumption 1 hold for our results, we have picked a small δ = 0.001 and σ2 = 1. The thresholds αm are set in two different ways. |