Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity
Authors: Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina Eldar, Tong Zhang
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Detailed numerical results are provided to back up our theory. |
| Researcher Affiliation | Academia | Zhuoran Yang ZY6@PRINCETON.EDU Zhaoran Wang ZHAORAN@PRINCETON.EDU Han Liu HANLIU@PRINCETON.EDU Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA Yonina C. Eldar YONINA@EE.TECHNION.AC.IL Department of EE Technion, Israel Institute of Technology, Haifa 32000, Israel Tong Zhang TZHANG@STAT.RUTGERS.EDU Department of Statistics, Rutgers University, Piscataway, New Jersey 08854, USA |
| Pseudocode | Yes | Algorithm 1 Proximal gradient algorithm for solving the 1-regularized problem in (1.2). Algorithm 2 The Barzilai-Borwein (BB) spectral approach for choosing αt in Line 1 of Algorithm 1. |
| Open Source Code | No | The paper describes algorithms and numerical experiments but does not provide any explicit statement or link for the availability of its source code. |
| Open Datasets | Yes | To show the effectiveness of the proposed method, we study the Computer Audition Lab 500-Song (CAL500) dataset (Turnbull et al., 2008), which can be obtained from the publicly available Mulan data library (Tsoumakas et al., 2011). |
| Dataset Splits | Yes | For the linear framework we apply the 1-regularized regresion (Lasso) (Tibshirani, 1996). ... The optimization problem of Lasso is also solved using Algorithm 1. We plot the 2-errors of these two techniques against the effective sample size in Figure 1-(c), which shows that the proposed method outperforms the linear approach. ... the regularization parameter of Lasso is selected via 5-fold cross-validation. |
| Hardware Specification | No | The paper discusses running numerical experiments but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for these computations. |
| Software Dependencies | No | The paper mentions the use of 'Lasso' and references algorithms like 'ISTA' and 'Spa RSA method' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The parameters of Algorithm 1 are chosen as αmin = 1/αmax = 1030, η = 2, M = 5, and ζ = tol = 10 5. The 2-errors reported are based on 100 independent experiments. We plot the 2-errors against the effective sample size log d/n in Figure 1. The figure illustrates that β β 2 grows sublinearly with |