Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma
Authors: Jason Vander Woude, Peter Dixon, A. Pavan, Jamie Radcliffe, N. V. Vinodchandran
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies replicability in machine learning tasks from a geometric viewpoint. Recent works have revealed the role of geometric partitions and Sperner s lemma (and its variations) in designing replicable learning algorithms and in establishing impossibility results. ... Our first contribution is a comprehensive understanding of the optimality of secluded partition constructions. Our second contribution is the discovery of a new neighborhood variant of the Sperner/KKM lemma. |
| Researcher Affiliation | Collaboration | Jason Vander Woude Sandia National Laboratories EMAIL Peter Dixon University of Toronto, Mississauga EMAIL A. Pavan Iowa State University EMAIL Jamie Radcliffe University of Nebraska-Lincoln EMAIL N. V. Vinodchandran University of Nebraska-Lincoln EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our paper does not include experiments requiring code. (From NeurIPS checklist answer) |
| Open Datasets | No | Our paper does not include experiments. (From NeurIPS checklist answer) |
| Dataset Splits | No | Our paper does not include experiments. (From NeurIPS checklist answer) |
| Hardware Specification | No | Our paper does not include experiments. (From NeurIPS checklist answer) |
| Software Dependencies | No | Our paper does not include experiments. (From NeurIPS checklist answer) |
| Experiment Setup | No | Our paper does not include experiments. (From NeurIPS checklist answer) |