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..
Learning convex polytopes with margin
Authors: Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an improved algorithm for properly learning convex polytopes in the realizable PAC setting from data with a margin. Our learning algorithm constructs a consistent polytope as an intersection of about t log t halfspaces with margins in time polynomial in t (where t is the number of halfspaces forming an optimal polytope). We also identify distinct generalizations of the notion of margin from hyperplanes to polytopes and investigate how they relate geometrically; this result may be of interest beyond the learning setting. |
| Researcher Affiliation | Academia | Lee-Ad Gottlieb Ariel University EMAIL Eran Kaufman Ariel University EMAIL Aryeh Kontorovich Ben-Gurion University EMAIL Gabriel Nivasch Ariel University EMAIL |
| Pseudocode | No | The paper describes algorithms in text (e.g., in Section 3.2 'Algorithms' and Theorem 7 proof) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not describe experiments with datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical validation on datasets. |
| Hardware Specification | No | This is a theoretical paper and does not describe specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with specific hyperparameters or training configurations. |