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..
Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space
Authors: Heng Lian
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 3 contains a simple numerical illustration, which is not used to verify the learning rate (which is difficult if not impossible) but just to illustrate the divide-and-conquer method can work reasonably. ... The simulation results are shown in Table 1 for τ = 0.5. We can examine the table in several ways. ... The estimation errors for different pairs of (n, m) when τ = 0.5. |
| Researcher Affiliation | Academia | Heng Lian City University of Hong Kong Shenzhen Research Institute, Shenzhen, China and Department of Mathematics, City University of Hong Kong, Hong Kong, China EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper uses synthetic data generated from the model yi = f0(xi) + (1 + xi)σ(ϵi Φ 1(τ)), where xi are generated uniformly on [0, 1], ϵi N(0, 1), with f0(x) = sin(2πx) and σ = 0.5. No publicly available dataset is used or provided. |
| Dataset Splits | No | The paper mentions that the tuning parameter λ is chosen 'to minimize the errors on independently generated test data'. However, it does not specify explicit training/validation/test splits (e.g., percentages or counts) for the main experiment or use cross-validation for model evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The tuning parameter λ is chosen to minimize the errors on independently generated test data. For the RKHS, we use the Sobolev space of the second order. The sample is generated from the model yi = f0(xi) + (1 + xi)σ(ϵi Φ 1(τ)), where we set f0(x) = sin(2πx) and σ = 0.5. The simulations are carried out for different combinations of n {32, 64, 128, 256, 512, 1024} and m {1, 2, 4, 8, 16, 32}. |