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..
Time--Data Tradeoffs by Aggressive Smoothing
Authors: John J Bruer, Joel A Tropp, Volkan Cevher, Stephen Becker
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 3 shows the results of a numerical experiment that compares the performance difference between current numerical practice and our aggressive smoothing approach. |
| Researcher Affiliation | Academia | John J. Bruer1,* Joel A. Tropp1 Volkan Cevher2 Stephen R. Becker3 1Dept. of Computing + Mathematical Sciences, California Institute of Technology 2Laboratory for Information and Inference Systems, EPFL 3Dept. of Applied Mathematics, University of Colorado at Boulder *EMAIL |
| Pseudocode | Yes | Algorithm 3.1 Auslender Teboulle applied to the dual-smoothed RLIP |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | In the experiment, we fix both the ambient dimension d = 40 000 and the normalized sparsity ρ = 5%. To test each smoothing approach, we generate and solve 10 random sparse vector recovery models for each value of the sample size m = 12 000,14 000,16 000,...,38 000. Each random model comprises a Gaussian measurement matrix A and a random sparse vector x whose nonzero entires are 1 with equal probability. |
| Dataset Splits | No | The paper describes generating random models for various sample sizes, but does not provide specific training/test/validation dataset splits from a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In the experiment, we fix both the ambient dimension d = 40 000 and the normalized sparsity ρ = 5%. We stop Algorithm 3.1 when the relative error x xk / x is less than 10 3. For the constant smoothing case, we choose µ = 0.1 based on the recommendation in [15]. We set the smoothing parameter µ = µ(m)/4. |