A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
Authors: Junhyung Park, Krikamol Muandet
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We derive a natural regression interpretation to obtain empirical estimates, and provide a thorough theoretical analysis thereof, including universal consistency. As natural by-products, we obtain the conditional analogues of the maximum mean discrepancy and Hilbert-Schmidt independence criterion, and demonstrate their behaviour via simulations. |
| Researcher Affiliation | Academia | Junhyung Park MPI for Intelligent Systems, Tübingen junhyung.park@tuebingen.mpg.de Krikamol Muandet MPI for Intelligent Systems, Tübingen krikamol@tuebingen.mpg.de |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses simulated data generated from specified distributions (e.g., Z N(0, 1), X = e 0.5Z2 sin(2Z) + NX), and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper simulates data but does not provide specific dataset split information (e.g., train/validation/test percentages or counts, or cross-validation setup) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In all simulations, we use the Gaussian kernel k X (x, x ) = k Y(x, x ) = k Z(x, x ) = e 1 2 σX x x 2 2 with hyperparameter σX = 0.1, and regularisation parameter λ = 0.01. with regularisation λn = 10 7n 1 4 . |