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..
Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery
Authors: Yingjie Wang, Hong Chen, Feng Zheng, Chen Xu, Tieliang Gong, Yanhong Chen
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on simulations and the CMEs analysis demonstrate the competitive performance of our approach for robust estimation and automatic structure discovery. |
| Researcher Affiliation | Academia | 1College of Informatics, Huazhong Agricultural University, China 2 College of Science, Huazhong Agricultural University, China 3 Department of Computer Science and Engineering, Southern University of Science and Technology, China 4 Department of Mathematics and Statistics, University of Ottawa, Canada 5 School of Computer Science and Technology, Xi an Jiaotong University, China 6 National Space Science Center, Chinese Academy of Sciences, China |
| Pseudocode | Yes | Algorithm 1: Prox-SAGA for MAM |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | Interplanetary CMEs (ICMEs) data are provided in The Richardson and Cane List (http://www.srl.caltech.edu/ACE/ASC/DATA/level3/ icmetable2.htm). From this link, we collect 137 ICMEs observations from 1996 to 2016. The features of CMEs are provided in SOHO LASCO CME Catalog (https://cdaw.gsfc. nasa.gov/CME_list/). In-situ solar wind parameters can be downloaded from OMNIWeb Plus (https://omniweb.gsfc.nasa.gov/). |
| Dataset Splits | Yes | Without loss of generality, we split each S(t) into the training set S(t) train and the validation set S(t) val with the same sample size n for subsequent analysis. |
| Hardware Specification | Yes | All experiments are implemented in MATLAB 2019b on an intel Core i7 with 16 GB memory. |
| Software Dependencies | Yes | All experiments are implemented in MATLAB 2019b on an intel Core i7 with 16 GB memory. |
| Experiment Setup | Yes | For the same hyper-parameters in Bi GL and MAM, we set Z = 3000, ยต = 10 3, M = 5, Q = 100 and ฯ = 2. We search the regularization parameter ฮป in the range of {10 4, 10 3, 10 2, 10 1}. Here, we assume the actual number of groups is known, i.e., L = L . The weight for each group is set to be ฯl = 1, l {1, ..., L}. Following the same strategy in [11], we choose the initialization ฯ(0) = Pฯ( 1LIP L + 0.01N(0P L, IP L)) RP L and ฮฝ(0) = (0.5, ..., 0.5)T RP . |