Demystifying the Optimal Performance of Multi-Class Classification
Authors: Minoh Jeong, Martina Cardone, Alex Dytso
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate the effectiveness of our theoretical results via experiments both on synthetic data under various noise settings and on real data. |
| Researcher Affiliation | Collaboration | Minoh Jeong Electrical and Computer Engineering University of Minnesota Minneapolis, MN 55455 jeong316@umn.edu Martina Cardone Electrical and Computer Engineering University of Minnesota Minneapolis, MN 55455 mcardone@umn.edu Alex Dytso Qualcomm Flarion Technology, Inc. Bridgewater, NJ 08807 odytso2@gmail.com |
| Pseudocode | No | The paper describes methods using mathematical definitions and theorems, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper cites third-party codebases for models used (e.g., "Vi T-Py Torch, 2020. https://github.com/jeonsworld/Vi T-pytorch," and "Py Torch_CIFAR10, 2021. https://github.com/huyvnphan/Py Torch_CIFAR10,"), but does not provide concrete access to the source code for the methodology described in this paper. |
| Open Datasets | Yes | We empirically validate our results using various datasets, namely: 1) a synthetic dataset with different noises, including one-hot labels; 2) two benchmark datasets CIFAR-10H [4] and Fashion-MNIST-H [46]; and 3) Movie Lens [38], a real-world dataset for movie recommendations. |
| Dataset Splits | No | The paper mentions using "synthetic data" and "benchmark datasets CIFAR-10H and Fashion-MNIST-H", which are variations of existing datasets, but does not explicitly provide specific training/validation/test dataset splits (e.g., percentages or sample counts) for their experiments. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' in relation to third-party implementations used for comparison models, but does not provide specific version numbers for software dependencies of its own methodology (e.g., 'PyTorch 1.9'). |
| Experiment Setup | Yes | Mo BK(ψC) uses K = n (with this choice, Theorem 2 ensures the asymptotic normality of Mo BK), the Euclidean distance for d, and r = 1/5. For different n, the parameters of Mo BK(ψC) are chosen as K = n, d is the Euclidean distance, and r = 1/5. We iterate the experiment 50 times for each n. We consider a 4-class classification problem with equiprobable classes C Cµ := {(µ, µ), ( µ, µ), ( µ, µ), (µ, µ)}, where µ > 0 is a parameter that controls the classification hardness. We generate the feature X R2 according to a 2-dimensional Gaussian distribution with mean c (i.e., a realization of C Cµ) and covariance matrix I2. |