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..
Mixed Regression via Approximate Message Passing
Authors: Nelvin Tan, Ramji Venkataramanan
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical results are validated by numerical simulations for mixed linear regression, max-affine regression, and mixture-of-experts. For max-affine regression, we propose an algorithm that combines AMP with expectation-maximization to estimate the intercepts of the model along with the signals. The numerical results show that AMP significantly outperforms other estimators for mixed linear regression and max-affine regression in most parameter regimes. |
| Researcher Affiliation | Academia | Nelvin Tan EMAIL Department of Engineering, University of Cambridge Cambridge, CB2 1PZ, United Kingdom Ramji Venkataramanan EMAIL Department of Engineering, University of Cambridge Cambridge, CB2 1PZ, United Kingdom |
| Pseudocode | Yes | Algorithm 1 Expectation-maximization approximate message passing (EM-AMP) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology described is publicly available or released. It mentions a preliminary version published in AISTATS 2023 but does not provide code access. |
| Open Datasets | No | The paper describes generating synthetic data for its simulations (e.g., "Xi i.i.d. N(0, Ip/n)", "ci i.i.d. Bernoulli(α)"). It does not refer to or provide access information for any existing public datasets. |
| Dataset Splits | No | The paper describes generating synthetic data for its numerical simulations and varies parameters like 'p' (signal dimension) and 'n' (number of observations) to control the sampling ratio 'δ = n/p'. It does not discuss conventional training/validation/test splits for specific datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU/CPU models, processors, or memory specifications. |
| Software Dependencies | No | The paper does not explicitly mention any specific software dependencies or their version numbers that would be required to reproduce the experiments. |
| Experiment Setup | Yes | In Figures 1, 2, and 3, we set the Bernoulli parameter α = 0.7 and choose the two signals to be jointly Gaussian, with their entries generated as (β(1) j , β(2) j ) i.i.d. N( 0 0 , j [p]). The signal dimension p = 500 and vary the value of n in our experiments. For the soft-thresholding denoiser fk... The tuning parameter ζ set to 1.1402. ...we execute EM-AMP with mmax = 5 and kmax = 5. |