Model Linkage Selection for Cooperative Learning
Authors: Jiaying Zhou, Jie Ding, Kean Ming Tan, Vahid Tarokh
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical studies are conducted to assess the performance of the proposed method. |
| Researcher Affiliation | Academia | Jiaying Zhou EMAIL School of Statistics University of Minnesota Minneapolis MN, 55455 Jie Ding EMAIL School of Statistics University of Minnesota Minneapolis MN, 55455 Kean Ming Tan EMAIL Department of Statistics University of Michigan Ann Arbor MI, 48109 Vahid Tarokh EMAIL Department of Electrical Engineering and Engineering Duke University Durham NC, 27708 |
| Pseudocode | Yes | Algorithm 1 Greedy Algorithm for Model Linkage Selection. Input: User-specified graph G, data D(κ), parameter vector θκ, parametric distribution p(κ) θκ ( | θκ, X(κ)), prior distribution πκ( ) on parameters θκ, for κ = 1, . . . , M. 1: Initialize the index ℓ= 1, linkage set ζ(1) = {1}. 2: for ℓ= 2, . . . , M do 3: Let NG(ζ(ℓ 1)) denote the neighboring set of ζ(ℓ 1) within G, namely the set of learners in G\ζ(ℓ 1) that have a model linkage with at least one learner in ζ(ℓ 1). 4: Calculate jopt = argmaxj NG(ζ(ℓ 1))p( κ ζ(ℓ 1)y(κ) | κ ζ(ℓ 1)X(κ), D(j)). 5: if p( κ ζ(ℓ 1)y(κ)| κ ζ(ℓ 1) X(κ)) p( κ ζ(ℓ 1)y(κ) | κ ζ(ℓ 1)X(κ), D(jopt)) break 6: Let ζ(ℓ) = {jopt} ζ(ℓ 1). 7: if ζ(ℓ) = C(G) break 8: end for 9: For a new predictor ex, let bp(ℓ) = p( | κ ζ(ℓ) D(κ), ex) and bπ(ℓ) = π( | κ ζ(ℓ) D(κ)). Output: Predictive distribution bp = bp(ℓ), posterior distribution bπ = bπ(ℓ), and model linkage graph b G. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only provides a link to the journal's attribution requirements page, not a code repository. |
| Open Datasets | Yes | We consider the Wisconsin Breast Cancer database (Mangasarian et al., 1995). |
| Dataset Splits | Yes | We randomly choose 100 samples as the test data for evaluating prediction accuracy. Then, the data are randomly divided into 10 learners, in which each learner has n samples. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We apply Algorithm 1 with the aforementioned user-specified graph and impose a multivariate Gaussian distribution, Np(0, 4Ip), as the prior distribution for the regression coefficients for all learners. |