Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
Authors: Junhyung Park, Krikamol Muandet
NeurIPS 2020 | Venue PDF | 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 EMAIL Krikamol Muandet MPI for Intelligent Systems, Tübingen EMAIL |
| 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 . |