Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space
Authors: Heng Lian
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 henglian@cityu.edu.hk |
| 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}. |