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..
Universality in Transfer Learning for Linear Models
Authors: Reza Ghane, Danil Akhtiamov, Babak Hassibi
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we validate our theoretical results through empirical experiments. |
| Researcher Affiliation | Academia | Reza Ghane Department of Electrical Engineering California Institute of Technology Pasadena, CA 91125 EMAIL Danil Akhtiamov Department of Computing + Mathematical Sciences California Institute of Technology Pasadena, CA 91125 EMAIL Babak Hassibi Department of Electrical Engineering Department of Computing + Mathematical Sciences California Institute of Technology Pasadena, CA 91125 EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code is attached to the supplementary material. |
| Open Datasets | No | The paper describes generating synthetic data from specified distributions (N(0,1), Ber(0.5), chi2(1)) rather than using a named, publicly accessible dataset with concrete access information. |
| Dataset Splits | No | The paper generates synthetic data for different values of `n` and `d` but does not specify explicit train/validation/test dataset splits (e.g., percentages or counts) as it relates to a fixed dataset. |
| Hardware Specification | Yes | We used CVXPY (Grant and Boyd [2014], Agrawal et al. [2018]) to solve (1) efficiently on a Laptop CPU. |
| Software Dependencies | Yes | We used CVXPY (Grant and Boyd [2014], Agrawal et al. [2018]) to solve (1) efficiently on a Laptop CPU. |
| Experiment Setup | Yes | To do so, we fixed π= 1000 and varied πacross different values... w0 is chosen according to Assumptions 3 in such a manner that ππ= 1... We also sampled the means Β΅1 and Β΅2 from N (0, 1/πIπ) with a cross-correlation π= E[Β΅1πΒ΅2π] = 0.9... For Figures 3, we fixed π= 0.8, 2, 5 respectively... |