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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
A first-order primal-dual method with adaptivity to local smoothness
Authors: Maria-Luiza Vladarean, Yura Malitsky, Volkan Cevher
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,343 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,353 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are provided for illustrating the practical performance of the algorithm. We now present some numerical experiments conducted for APDA. |
| Researcher Affiliation | Academia | Maria-Luiza Vladarean Yura Malitsky Volkan Cevher {maria-luiza.vladarean, volkan.cevher}@epfl.ch EMAIL LIONS, École Polytechnique Fédérale de Lausanne, Switzerland Linköping University, Sweden |
| Pseudocode | Yes | Algorithm 1 Adaptive Primal Dual Algorithm (APDA) Input: x0 X, y0 Y, τinit > 0, τ0 = , θ0 = 1, β > 0, c (0, 1) x1 = x0 τinit( f(x0) + AT y0) for k = 1, 2, . . . do Set τk = min 1 2 L2 k+(β/(1 c)) A 2 , τk 1 p , σk = βτk, θk = τk τk 1 xk = xk + θk(xk xk 1) yk+1 = proxσkg (yk + σk A xk) xk+1 = xk τk( f(xk) + AT yk+1) end for |
| Open Source Code | Yes | 1See https://github.com/mvladarean/adaptive_pda. |
| Open Datasets | Yes | We consider the problem of sparse binary Logistic Regression on 4 LIBSVM datasets [Chang and Lin, 2011] |
| Dataset Splits | No | The paper mentions using LIBSVM datasets but does not provide specific details on training, validation, or test splits such as percentages or sample counts. |
| Hardware Specification | Yes | The experiments were implemented in Python 3.9 and executed on a Mac Book Pro with 32 GB RAM and a 2,9 GHz 6-Core Intel Core i9 processor. |
| Software Dependencies | No | The experiments were implemented in Python 3.9 and executed on a Mac Book Pro with 32 GB RAM and a 2,9 GHz 6-Core Intel Core i9 processor. |
| Experiment Setup | Yes | We choose λ = 0.005 QT b. For APDA we perform a parameter sweep over β [1e-3, 1e6] for each dataset and settle for: β = 2.68e3 for ijcnn; β = 5.18e4 for a9a; β = 3.16e1 for mushrooms; β = 3.73e-1 for covtype. For CVA we sweep p [1e-3, 1e6] and set τ = 1 A /p+L and σ = 1 p A... |