A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
Authors: Shivam Kumar, Yun Yang, Lizhen Lin
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression... Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE)... Finally, in our numerical studies, we demonstrate the effective implementation of the proposed approach using both synthetic and realworld datasets, which also provide complementary validation to our theoretical findings. |
| Researcher Affiliation | Academia | 1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, USA 2Department of Mathematics, University of Maryland, College Park, USA. |
| Pseudocode | No | The paper describes algorithms and models (e.g., conditional variational auto-encoder architecture, Multr m, Monr m,γ) in paragraph text and mathematical notation, but it does not contain any clearly labeled pseudocode or algorithm blocks with structured, step-by-step procedures. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | We utilized the widely used MNIST dataset for two purposes: to demonstrate the generalizability of our approach to a benchmark image dataset... |
| Dataset Splits | Yes | The sample size used for simulation is 5000, with a training-to-testing ratio of 4 : 1... We utilized a sample size of 5000 for simulation, with a training-to-testing ratio of 4 : 1. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper describes the neural network architectures and experimental settings but does not specify any software libraries or frameworks with their version numbers. |
| Experiment Setup | Yes | The neural architecture for both the encoder and decoder consists of two deep layers, i.e., L = 2. The hyperparameters are as follows: renc = (p + 1, 10, 10) for µϕ and Σϕ, and rdec = (10 + p, 10, 1) for g. ... We employ a batch size of 64 with a learning rate of 10^-3. |