Estimating Mutual Information for Discrete-Continuous Mixtures
Authors: Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove the consistency of this estimator theoretically as well as demonstrate its excellent empirical performance. This problem is relevant in a wide-array of applications, where some variables are discrete, some continuous, and others are a mixture between continuous and discrete components. ... Section 5 contains the results of our detailed synthetic and real-world experiments testing the efficacy of the proposed estimator. |
| Researcher Affiliation | Academia | Weihao Gao Department of ECE Coordinated Science Laboratory University of Illinois at Urbana-Champaign wgao9@illinois.edu Sreeram Kannan Department of Electrical Engineering University of Washington ksreeram@uw.edu Sewoong Oh Department of IESE Coordinated Science Laboratory University of Illinois at Urbana-Champaign swoh@illinois.edu Pramod Viswanath Department of ECE Coordinated Science Laboratory University of Illinois at Urbana-Champaign pramodv@illinois.edu |
| Pseudocode | Yes | Algorithm 1 Mixed Random Variable Mutual Information Estimator |
| Open Source Code | No | The paper does not provide any statements about open-sourcing the code for the described methodology, nor does it include any links to a code repository. |
| Open Datasets | Yes | Gene regulatory network inference. ... Instead we resorted to a challenge dataset for reconstructing regulatory networks, called the DREAM5 challenge [30]. The simulated (insilico) version of this dataset contains gene expression for 20 genes with 660 data point containing various perturbations. |
| Dataset Splits | No | The paper describes synthetic data generation and real-world datasets but does not specify training, validation, or test splits for any of its experiments. It focuses on mean squared error versus sample size. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | No | The paper describes the characteristics of the synthetic data generated for experiments (e.g., 'X is uniformly distributed over integers {0, 1, . . . , m 1} and Y is uniformly distributed over the range [X, X + 2] for a given X'). However, it does not provide specific hyperparameter values or system-level training settings used for its estimator in these experiments. |