Errors-in-variables Fr\'echet Regression with Low-rank Covariate Approximation
Authors: Dogyoon Song, Kyunghee Han
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, our numerical experiments provide empirical evidence that supports the theoretical findings, demonstrating the superior performance of our approach. |
| Researcher Affiliation | Academia | Dogyoon Song Department of EECS University of Michigan Ann Arbor, MI 48109, USA dogyoons@umich.edu Kyunghee Han Department of Math, Stat and Comp Sci University of Illinois at Chicago Chicago, IL 60607, USA hankh@uic.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper states that the datasets are 'generated as follows' and provides the methodology for generating synthetic data, but it does not provide access (e.g., a link, DOI, or citation to a repository) to specific pre-generated datasets used in the experiments. |
| Dataset Splits | No | The paper mentions a 'training set Dn' and a 'test set Dnew N' but does not explicitly describe a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Experimental setup. We consider combinations of p {150, 300, 600} and n {100, 200, 400}. The datasets Dn = {(Xi, Yi) : i [n]} and e Dn = {(Zi, Yi) : i [n]} are generated as follows. Let Xi Np 0p, Σ be IID multivariate Gaussian with mean 0p and covariance Σ such that spec (Σ) = {κj > 0 : j [p]} is an exponentially decreasing sequence... We generate Zi following (5) under two scenarios εij IID N 0, σ2 ε and εij IID Laplace 0, σε , respectively. Lastly, given X = x, let Y be the distribution function of N µα,β(x)+η, τ 2 , where (i) µα,β(x) = α + β x with α = 1 and β = p 1/2 1p; (ii) η N 0, σ2 η ; and (iii) τ 2 IG(s1, s2), an inverse gamma distribution with shape s1 and scale s2. We set σ2 ε = 0.052. We set σ2 η = 0.52, and (s1, s2) = (18, 17). Tuning parameter λ) For simplicity, we chose a universal threshold value as ˆλn = arg minλ Λ MSPE(φ(λ) e Dn), where Λ is a fine grid on 0, pλ1 p/n . |