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.
Scaling Up Ordinal Embedding: A Landmark Approach
Authors: Jesse Anderton, Javed Aslam
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,353 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,374 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 | Figure 2 exhibits L-SOE for a variety of values of n and d, on two simulated datasets a set of points sampled from a uniform ball, and a set of points sampled from a Gaussian Mixture Model (GMM) with 10 components. ... We also tested LLOE on two real datasets. We embed 20 Newsgroups (n = 18, 846, d = 101, 631 TF-IDF scores) and MNIST Digits (n = 70, 000, d = 784 pixels), and compare the results to LSA in Figure 4. |
| Researcher Affiliation | Academia | 1College of Computer and Information Science, Northeastern University, Boston, Massachusetts. Correspondence to: Jesse Anderton <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 LLOE(n, d, L, o) |
| Open Source Code | Yes | 1 Available at https://github.com/jesand/lloe. |
| Open Datasets | Yes | on two simulated datasets a set of points sampled from a uniform ball, and a set of points sampled from a Gaussian Mixture Model (GMM) with 10 components. ... We also tested LLOE on two real datasets. We embed 20 Newsgroups (n = 18, 846, d = 101, 631 TF-IDF scores) and MNIST Digits (n = 70, 000, d = 784 pixels) |
| Dataset Splits | No | The paper mentions evaluating on datasets and metrics like 'Probability perr that a random triplet is incorrect in the embedding', but does not provide specific details on training/validation/test splits (e.g., percentages, sample counts, or explicit instructions for partitioning the data). |
| Hardware Specification | Yes | Embeddings run on a late 2013 15 quad core Mac Book Pro with 2 GHz Intel Core i7 CPU and 16GB of RAM. |
| Software Dependencies | No | The paper mentions software like 'sklearn.datasets.make blobs()' and 'L-BFGS', but does not provide specific version numbers for any software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | Each embedding proceeds for up to 1,000 rounds of L-BFGS, with early termination if no loss decrease is observed. We report the best embedding from 20 random initializations. |