Learning a 1-layer conditional generative model in total variation
Authors: Ajil Jalal, Justin Kang, Ananya Uppal, Kannan Ramchandran, Eric Price
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Simulations |
| Researcher Affiliation | Academia | Ajil Jalal Justin Kang UC Berkeley {ajiljalal, justin_kang}@berkeley.edu Ananya Uppal UT Austin ananya.uppal09@gmail.com Kannan Ramchandran UC Berkeley kannanr@eecs.berkeley.edu Eric Price UT Austin ecprice@cs.utexas.edu |
| Pseudocode | No | The paper describes methods and proofs but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https: //github.com/basics-lab/learning Generative Models.git. |
| Open Datasets | No | The paper describes generating synthetic data from distributions like x N(0, Ik) and x k Lap(0, 1) or Normal mixtures for simulations, but does not use or provide concrete access information for a pre-existing public dataset. |
| Dataset Splits | No | The paper describes generating 'n' samples for its simulations but does not specify any training, validation, or test dataset splits, percentages, or cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications such as GPU or CPU models, memory details, or cloud computing instance types used for running the simulations. |
| Software Dependencies | No | The paper mentions using 'MATLAB integral function' but does not specify its version or any other software dependencies with specific version numbers. |
| Experiment Setup | Yes | In these experiment, we set d = 1, and plot the results for various values of the number of samples n in Figure 2a and various values of the input dimension k in Figure 2b. For each plot, we fix the true σ = 1 and the w = 1k 1. In each case the MLE is solved via gradient descent with backtracking line search, and we check a first order condition w,σ log pw,σ((y | x)) 2 < δ = 10 3 as the exit condition. |