Learning With Subquadratic Regularization : A Primal-Dual Approach
Authors: Raman Sankaran, Francis Bach, Chiranjib Bhattacharyya
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments To illustrate the efficiency of CP-η and ADMM-η over existing algorithms, we choose the aforementioned tree-sparsity inducing norm ΩH (Example 1)... Setup. We perform numerical simulations2 by generating synthetic data... We make the following inferences from the simulation plots given in Figure 1. |
| Researcher Affiliation | Collaboration | Raman Sankaran1,3 , Francis Bach2 and Chiranjib Bhattacharyya3 1Linked In, Bengaluru 2INRIA Ecole Normale Sup erieure PSL Research University, Paris 3Indian Institute of Science, Bengaluru |
| Pseudocode | Yes | Algorithm 1 CP [Chambolle and Pock, 2011]... Algorithm 2 ADMM-η... Algorithm 3 CP-η |
| Open Source Code | No | The paper does not provide any specific links to open-source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | No | We perform numerical simulations by generating synthetic data. Following [Bach et al., 2011], we generate X Rn d as Xij N(0, 1). |
| Dataset Splits | No | The paper performs numerical simulations by generating synthetic data and sets parameters like n, d, and λ, but it does not specify train, validation, or test dataset splits or cross-validation settings. |
| Hardware Specification | Yes | Conducted on a Ubuntu PC with Core i7 processor, 8G RAM. |
| Software Dependencies | No | The paper mentions running experiments on a 'Ubuntu PC' but does not specify any software dependencies (e.g., libraries, frameworks, or programming languages) with their version numbers. |
| Experiment Setup | Yes | We fixed n = 1000, d = 15000, λ = 0.01, and the convergence criteria was the relative duality gap (with threshold ϵ = 10 4). |