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].
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View
Authors: Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present and discuss numerical simulations that corroborate our theoretical findings. |
| Researcher Affiliation | Academia | Christos Thrampoulidis UC, Santa Barbara EMAIL Samet Oymak UC, Riverside EMAIL Mahdi Soltanolkotabi University of Southern California EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data based on Gaussian Mixture Model (GMM) and Multinomial Logit Model (MLM) specifications rather than using a publicly available dataset. Therefore, no concrete access information for a public dataset is provided. |
| Dataset Splits | No | The paper describes data generation and test error but does not explicitly specify training/validation/test dataset splits with percentages, sample counts, or references to predefined standard splits for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Figures 1 and 2 focus on GMM with k = 9 classes, d = 300 and µi 2 = 15. To model different class prior probabilities, we use the distribution 1 = 2 = 3 = 0.5, 4 = 0.5, 5 = 0.5, 6 = 0.25, 7 = 0.25, 8 = 0.25, 9 = 1 21. We consider three scenarios: (a) orthogonal means, equal prior ( i = 1 9); (b) orthogonal means, different prior; (c) correlated means with pairwise correlation coefficient equal to 0.5 (i.e., µi,µj ( µi 2 µj 2) = 0.5 for i j) and different priors as discussed above. Figure 3 focuses on orthogonal classes with varying number of classes k where µi 2 = 15 and d {50,100,200} with kd n = kγ fixed at kγ = 20 11. Figure 4 provides experiments on MLM with k = 9 orthogonal classes. Unlike GMM, CE achieves the best performance in MLM. In Figure 4 (a), classes have same norms µi 2 = 10, while in Figure 4 (b) we have quadrupled the norms of classes 7,8,9 and doubled the norms of classes 4,5,6. |