Approximate Supermodularity Bounds for Experimental Design
Authors: Luiz Chamon, Alejandro Ribeiro
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we illustrate the previous results in some numerical examples. To do so, we draw the elements of Ae from an i.i.d. zero-mean Gaussian random variable with variance 1/p and p = 20. The noise {ve} are also Gaussian random variables with Re = σ2 v I. We take σ2 v = 10 1 in high SNR and σ2 v = 10 in low SNR simulations. |
| Researcher Affiliation | Academia | Luiz F. O. Chamon and Alejandro Ribeiro Electrical and Systems Engineering University of Pennsylvania {luizf,aribeiro}@seas.upenn.edu |
| Pseudocode | No | The paper describes a greedy solution iteratively in text and with an equation, but does not present it as a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the methodology described in this paper is openly available. |
| Open Datasets | Yes | In the following example, we use a subset of the Each Movie dataset [23] to illustrate how greedy experimental design can be applied to address this problem. ... [23] Digital Equipment Corporation, Each Movie dataset, http://www.gatsby.ucl.ac.uk/~chuwei/data/Each Movie/. |
| Dataset Splits | No | The paper mentions 'a training and test set containing 9000 and 3000 users respectively' but does not explicitly state a validation split or its size. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | To do so, we draw the elements of Ae from an i.i.d. zero-mean Gaussian random variable with variance 1/p and p = 20. The noise {ve} are also Gaussian random variables with Re = σ2 v I. We take σ2 v = 10 1 in high SNR and σ2 v = 10 in low SNR simulations. The experiment pool contains #E = 200 experiments. ... Starting with A-optimal design, we display the bound from Theorem 3 in Figure 1a for multivariate measurements of size ne = 5 and designs of size k = 40. ... We also let H = I and take a non-informative prior θ = 0 and Rθ = σ2 θI with σ2 θ = 100. |