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 [1].
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
| 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... |