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..
Approximating Hierarchical MV-sets for Hierarchical Clustering
Authors: Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical results that demonstrate the superiority of our method over existing ones. |
| Researcher Affiliation | Academia | Assaf Glazer Omer Weissbrod Michael Lindenbaum Shaul Markovitch Department of Computer Science, Technion Israel Institute of Technology EMAIL |
| Pseudocode | No | The paper describes methods in prose but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The olive oil dataset [10] consists of 572 olive oil examples, with 8 features each, from 3 regions in Italy (R1, R2, R3), each one further divided into 3 sub-areas. |
| Dataset Splits | Yes | We split the data X into two equal-sized train and test sets, and construct a tree using the train set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions specific methods and algorithms but does not list any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | 20 i.i.d. points were sampled for training our q-OCSVM method, with α1 = 0.25, α2 = 0.5, α3 = 0.75 (3-quantiles), and with a bandwidth γ, which results in a cluster tree with 3 modes. |