Adaptive Algorithms for Relaxed Pareto Set Identification
Authors: Cyrille KONE, Emilie Kaufmann, Laura Richert
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase the good practical performance of Adaptive Pareto Exploration on a real-world scenario, in which we adaptively explore several vaccination strategies against Covid-19 in order to find the optimal ones when multiple immunogenicity criteria are taken into account. ... Then, Section 5 presents the result of a numerical study on synthetic datasets, one of them being inspired by a Covid-19 vaccine clinical trial. It showcases the good empirical performance of APE compared to existing algorithms, and illustrates the impact of the different relaxations. |
| Researcher Affiliation | Academia | 1 Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9198-CRISt AL, F-59000 Lille, France 2 Univ. Bordeaux, Inserm, Inria, BPH, U1219, Sistm, F-33000 Bordeaux, France |
| Pseudocode | Yes | Algorithm 1: '1-APE-k: Adaptive Pareto Exploration for '1-PSI-k |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code available or include a link to a code repository. |
| Open Datasets | No | The paper references the COV-BOOST [24] study and states it uses its average (log) outcomes and variances to simulate a multivariate Gaussian bandit. While the study [24] is cited, the paper does not provide concrete access (e.g., a direct link, DOI, or repository) to the processed or raw dataset used for their simulations/experiments. |
| Dataset Splits | No | The paper describes simulating data (from COV-BOOST study and random Bernoulli instances) and running experiments, but it does not specify any training, validation, or test dataset splits for these simulations. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper describes its algorithms and experiments but does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | Yes | In this experiment, we set '1 = 0, δ = 0.1 and compare PSI-Unif-Elim to 0-APE-k (called APE-k in the sequel) for different values of k. The empirical distribution of the sample complexity of the algorithms, averaged over 2000 independent runs, are reported in Figure 1. ... We ran the previous algorithms on 2000 randomly generated multi-variate Bernoulli instances, with K = 5 arms and different values of the dimension D. We set δ = 0.1 and '1 = 0.005 (to have reasonable running time). |