Multi-Fidelity Best-Arm Identification
Authors: Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 riccardo.poiani@polimi.it Alberto Maria Metelli DEIB, Politecnico di Milano albertomaria.metelli@polimi.it Marcello Restelli DEIB, Politecnico di Milano marcello.restelli@polimi.it |
| 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. |