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 [1].
Towards a Combinatorial Characterization of Bounded-Memory Learning
Authors: Alon Gonen, Shachar Lovett, Michal Moshkovitz
NeurIPS 2020 | Venue PDF | 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 ο¬rst 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. |