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..
Refined Learning Bounds for Kernel and Approximate $k$-Means
Authors: Yong Liu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we validate our theoretical findings by performing experiments on both simulated and real data. |
| Researcher Affiliation | Academia | Yong Liu1,2 1Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 2Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China EMAIL |
| Pseudocode | Yes | For the completeness, we briefly describe the improved k-means++ in the following, please refer to [25] for more details. 1: If |C| < k, add a sampled point x S with probability cost({ψ(x)}, C) P x S cost({ψ(x)}, C), where cost(P, C) = X xi P min c C Φi c , and add ψ(x) to C. 2: If |C| k, sample x S with probability cost({ψ(x)},C) P x S cost({ψ(x)},C), check whether there exists a point c C such that cost(S, C\{c} {ψ(x)}) < cost(S, C). If this is the case, we replace c by the point in C that reduces the cost function by the largest amount. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of its own source code. |
| Open Datasets | Yes | We use 6 publicly avaiable datasets, dna, segment, mushrooms, mnist, skin-nonskin and covtype, from the LIBSVM Data 2. |
| Dataset Splits | Yes | We generate Pk i=1 |Ci| samples of k clustering centers for training and 10,000 samples for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, or specific libraries with their versions). |
| Experiment Setup | No | The paper mentions using a "Gaussian kernel κ(x, x ) = exp x x 2 /σ2" but does not specify the value of σ (sigma) or any other hyperparameters for the kernel or for Lloyd's algorithm used in the experiments, making it not fully reproducible. |