An active learning framework for multi-group mean estimation
Authors: Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |