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].
When are Local Queries Useful for Robust Learning?
Authors: Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study learning models where the learner is given more power through the use of local queries, and give the ๏ฌrst distribution-free algorithms that perform robust empirical risk minimization (ERM) for this notion of robustness. [...] We then bound the query complexity of these algorithms based on online learning guarantees and further improve these bounds for the special case of conjunctions. We ๏ฌnish by giving robust learning algorithms for halfspaces with margins on both {0, 1}n and Rn. |
| Researcher Affiliation | Academia | Pascale Gourdeau University of Oxford EMAIL Varun Kanade University of Oxford EMAIL Marta Kwiatkowska University of Oxford EMAIL James Worrell University of Oxford EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. It discusses concept classes and distributions abstractly, but does not provide information on publicly available datasets for empirical training. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) is provided as the paper is theoretical and does not conduct experiments on specific datasets. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments are mentioned, as the paper is theoretical and does not involve empirical experiments. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are mentioned, as the paper is theoretical and does not involve empirical experiments. |
| Experiment Setup | No | No specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) are provided, as the paper is theoretical and does not involve empirical experiments. |