Universality laws for Gaussian mixtures in generalized linear models
Authors: Yatin Dandi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro, Lenka Zdeborová
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 2: Illustration of the universality scenario described in Fig.1. Logistic (left) & ridge (right) regression test (up) and training (bottom) errors are shown versus the sample complexity α = n/d for an odd vs. even binary classification task on two data models: Blue dots data generated from a conditional GAN [41] trained on the fashion-MNIST dataset [45] and pre-processed with a random features map x 7 tanh(Wx) with Gaussian weights W R1176 784; Red dots are the 10clusters Gaussian mixture model with means and covariances matching each fashion-MNIST cluster conditioned on labels (ℓ2 regularization is λ = 10 4). Details on the simulations are discussed in Appendix D. |
| Researcher Affiliation | Academia | Yatin Dandi, Ludovic Stephan, Florent Krzakala Idephics, EPFL, Switzerland Bruno Loureiro DI, Ecole Normale Superieure, Paris, France Lenka Zdeborova SPOC, EPFL, Switzerland |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about making its source code available or links to a code repository for its methodology. |
| Open Datasets | Yes | Blue dots data generated from a conditional GAN [41] trained on the fashion-MNIST dataset [45] and pre-processed with a random features map x 7 tanh(Wx) with Gaussian weights W R1176 784; Red dots are the 10clusters Gaussian mixture model with means and covariances matching each fashion-MNIST cluster conditioned on labels (ℓ2 regularization is λ = 10 4). Details on the simulations are discussed in Appendix D. |
| Dataset Splits | No | The paper mentions 'training data' but does not specify the explicit train/validation/test split percentages, sample counts, or refer to any predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | Yes | Figure 2: Illustration of the universality scenario described in Fig.1. Logistic (left) & ridge (right) regression test (up) and training (bottom) errors are shown versus the sample complexity α = n/d for an odd vs. even binary classification task on two data models: Blue dots data generated from a conditional GAN [41] trained on the fashion-MNIST dataset [45] and pre-processed with a random features map x 7 tanh(Wx) with Gaussian weights W R1176 784; Red dots are the 10clusters Gaussian mixture model with means and covariances matching each fashion-MNIST cluster conditioned on labels (ℓ2 regularization is λ = 10 4). Details on the simulations are discussed in Appendix D. |