Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Generative Local Metric Learning for Kernel Regression
Authors: Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank Park, Daniel D. Lee
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 14,798 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,810 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Masashi Sugiyama RIKEN / The University of Tokyo, Japan EMAIL Kee-Eung Kim KAIST, Rep. of Korea EMAIL Frank C. Park Seoul National University, Rep. of Korea EMAIL Daniel D. Lee University of Pennsylvania, USA EMAIL |
| 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. |