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..
Implicit Regularization for Optimal Sparse Recovery
Authors: Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our findings with numerical experiments and compare our algorithm against explicit 1 penalization. |
| Researcher Affiliation | Academia | 1 Department of Statistics, 2 Department of Computer Science University of Oxford |
| Pseudocode | Yes | Algorithm 1. Let α, η > 0 be two given parameters. Let u0 = v0 = α and for all t 0 we let mt = 1. Perform the updates given in (1). Algorithm 2. Let α, τ N and w max ˆz 2w max be three given parameters. Set η = 1 20ˆz and u0 = v0 = α. Perform the updates in (1) with m0 = 1 and mt adaptively defined as follows: 1. Set mt = mt 1. 2. If t = mτ log α 1 for some natural number m 2 then let mt,j = 2mt 1,j for all j such that u2 t,j v2 t,j 2 m 1ˆz. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a link or explicit statement) for its own source code. |
| Open Datasets | No | The paper describes generating synthetic data for its simulations: "For each run the entries of X are sampled as i.i.d. Rademacher random variables and the noise vector ξ follows i.i.d. N(0, σ2) distribution." It does not use or provide access to any publicly available dataset. |
| Dataset Splits | Yes | Among the 200 obtained models we choose the one with the smallest error on a validation dataset of size n/4. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., libraries, frameworks, or programming language versions). |
| Experiment Setup | Yes | Unless otherwise specified, the default values for simulation parameters are n = 500, d = 104, k = 25, α = 10 12, γ = 1, σ = 1 and for Algorithm 2 we set τ = 10. |