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..
An active learning framework for multi-group mean estimation
Authors: Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our findings by numerical experiments presented in Section 6, which show that our theoretical regret bounds match empirical convergence rates in both cases of finite and infinite p-norms. We also provide examples showing that for finite p-norms, the smallest variance affects the regret, even when the feedback is Gaussian. This is in contrast to the case of the infinite norm, where it is proven [13] that under Gaussian feedback, the algorithm is not affected by the smallest variance. |
| Researcher Affiliation | Academia | Abdellah Aznag Rachel Cummings Adam N. Elmachtoub Department of Industrial Engineering and Operational Research Columbia University {aa4683, rac2239, ae2516} @columbia.edu |
| Pseudocode | Yes | Algorithm 1 Variance-UCB (p, T, G, c1, c2) |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing its code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper uses synthetic data generated from Gaussian distributions for its numerical studies: "In all the experiments Dg follow Gaussian distributions." It does not refer to publicly available, named datasets. |
| Dataset Splits | No | The paper describes generating data from Gaussian distributions but does not specify training, validation, or test splits for this generated data. It directly evaluates the algorithm on these simulated scenarios. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory, cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for running its experiments (e.g., Python, PyTorch, or specific solvers). |
| Experiment Setup | Yes | Except where they are varied, the default parameter settings are T = 105, p = 2, G = 2, with the respective data distributions of groups 1 and 2 as N(1, 1) and N(2, 2.5), satisfying c1 = c2 = 5. |