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..
On the Complexity of PAC Learning in Hilbert Spaces
Authors: Sergei Chubanov
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our contribution to this line of research is the following: First, we propose a new algorithm allowing to improve existing bounds on the running time of PAC learning algorithms for the finite-dimensional case. Second, our algorithm is applicable to infinite-dimensional Hilbert spaces, in which case it guarantees similar complexity bounds in terms of a model of computation including inner products as elementary operations. ... Theorem 1 If there is a consistent Ī³-separating t-polyhedron, then the running time of Algorithm 2 is bounded by m O(tĪ³ 2 logm(Ī³ 1) ). |
| Researcher Affiliation | Industry | Sergei Chubanov Bosch Center for Artificial Intelligence, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1: Linear-programming (LP) algorithm; Algorithm 2: Separation algorithm; Algorithm 3: Improper-separation algorithm |
| Open Source Code | No | The paper does not provide any specific links or explicit statements regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and defines an 'instance space X' and concepts like 'drawing a sample', but does not refer to or provide access information for any specific public dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical algorithms and their complexity bounds, and does not report on empirical experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper describes theoretical algorithms and their complexity, and does not report on empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and complexity analysis; it does not provide details regarding experimental setup, hyperparameters, or training configurations. |