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.
Optimal Coresets for Low-Dimensional Geometric Median
Authors: Peyman Afshani, Chris Schwiegelshohn
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,657 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,913 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate coresets for approximating the cost with respect to median queries. In this problem, we are given a set of points P Rd and median queries are P p P p c for any point c Rd. Our goal is to compute a small weighted summary S P such that the cost of any median query is approximated within a multiplicative (1 ε) factor. We provide matching upper and lower bounds on the number of points contained in S of the order Θ ε d/(d+1) . |
| Researcher Affiliation | Academia | 1Department of Computer Science, Aarhus University, Denmark. |
| Pseudocode | No | The paper describes algorithms and constructions conceptually and mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available, nor does it provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. Therefore, it does not provide information about training dataset splits. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. Therefore, it does not provide information about validation dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about hardware used for computational experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers, as it focuses on theoretical proofs and algorithms rather than implementation details. |
| Experiment Setup | No | The paper focuses on theoretical contributions and does not describe an experimental setup with hyperparameters or training configurations. |