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..
Dimensionality Reduction for the Sum-of-Distances Metric
Authors: Zhili Feng, Praneeth Kacham, David Woodruff
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments to empirically verify that we can attain a non-trivial amount of data reduction while still being able to compute an approximate sum of distances to a kdimensional shape. In our experiments, we set n = 10000 and k = 5. We use various subspaces to compute an approximation to the sum of distances to a k center set. |
| Researcher Affiliation | Academia | 1 Carnegie Mellon University, Pittsburgh, USA. |
| Pseudocode | Yes | Algorithm 1 POLYAPPROX; Algorithm 2 EPSAPPROX; Algorithm 3 DIMENSIONREDUCTION; Algorithm 4 COMPLETEDIMREDUCE |
| Open Source Code | Yes | An implementation of our Algorithm 3 and code for our experiments is available at here2. https://gitlab.com/praneeth10/ dimensionality-reduction-for-sum-of-distances |
| Open Datasets | Yes | We run our dimensionality reduction algorithm on a randomly chosen subset A of size 10000 of the Cover Type dataset (Dua and Graff, 2017). URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | No | The paper mentions using a subset of the Cover Type dataset but does not specify any training, validation, or test splits, or the use of cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments (e.g., libraries, frameworks). |
| Experiment Setup | No | The paper mentions setting a "target dimension of 100" in the experiments, but it does not provide comprehensive details on other experimental setup parameters like learning rates, batch sizes, optimizers, or training schedules. |