Towards a Combinatorial Characterization of Bounded-Memory Learning
Authors: Alon Gonen, Shachar Lovett, Michal Moshkovitz
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove both upper and lower bounds for our candidate solution, that match in some regime of parameters. This is the first characterization of strong learning under space constraints in any regime. In this section we prove our upper bounds: Theorem 1 and Theorem 3. In this section we prove our lower bounds: Theorem 2 and Theorem 4. |
| Researcher Affiliation | Academia | Alon Gonen Shachar Lovett Michal Moshkovitz University of California San Diego |
| Pseudocode | No | The paper describes algorithms like Boosting-By-Majority conceptually and references other works for details, but it does not contain structured pseudocode or algorithm blocks within its text. |
| Open Source Code | No | The paper is theoretical and does not describe a software implementation or provide any links or statements about releasing source code for a methodology. |
| Open Datasets | No | The paper is purely theoretical and does not perform experiments involving datasets, so no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with data, thus it does not mention training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments, so no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, therefore no experimental setup details such as hyperparameters or training settings are provided. |