Generative Local Metric Learning for Kernel Regression
Authors: Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank Park, Daniel D. Lee
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed algorithm is evaluated using both synthetic and real datasets. For real-data experiments, we used the Delve datasets (Abalone, Bank-8fm, Bank-32fh, CPU), UCI datasets (Community, Naval C, Naval T, Protein, Slice), KEEL datasets (Ailerons, Elevators, Puma32h) [1], and datasets from a previous paper (Pendulum, Pol) [15]. |
| Researcher Affiliation | Academia | Yung-Kyun Noh Seoul National University, Rep. of Korea nohyung@snu.ac.kr Masashi Sugiyama RIKEN / The University of Tokyo, Japan sugi@k.u-tokyo.ac.kr Kee-Eung Kim KAIST, Rep. of Korea kekim@cs.kaist.ac.kr Frank C. Park Seoul National University, Rep. of Korea fcp@snu.ac.kr Daniel D. Lee University of Pennsylvania, USA ddlee@seas.upenn.edu |
| Pseudocode | Yes | Algorithm 1 Generative Local Metric Learning for NW Regression |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement about code release. |
| Open Datasets | Yes | For real-data experiments, we used the Delve datasets (Abalone, Bank-8fm, Bank-32fh, CPU), UCI datasets (Community, Naval C, Naval T, Protein, Slice), KEEL datasets (Ailerons, Elevators, Puma32h) [1], and datasets from a previous paper (Pendulum, Pol) [15]. |
| Dataset Splits | No | The paper states: 'we choose h from a pre-chosen validation set.' While it mentions using a validation set, it does not provide specific details on the dataset split, such as percentages, sample counts, or the methodology used for splitting. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Using a Gaussian model with regularized maximum likelihood estimated parameters, we apply a metric which minimizes the bias with a fixed γ = max(|λ1|, |λ2|) 10 2, and we choose h from a pre-chosen validation set. |