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..
Universal Rates for Interactive Learning
Authors: Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 3. If you ran experiments... (a) 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] |
| Researcher Affiliation | Collaboration | Steve Hanneke Purdue University EMAIL Amin Karbasi Yale University, Google Research EMAIL Shay Moran Technion, Google Research EMAIL Grigoris Velegkas Yale University EMAIL |
| Pseudocode | Yes | Figure 2: Arbitrarily Fast Rates Algorithm; Figure 3: Interactive Learning with Partial Concept Classes; Figure 4: Exponential Rates Algorithm; Figure 5: Aggregate Function Subroutine |
| Open Source Code | No | 3. If you ran experiments... (a) 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 describes a theoretical framework involving 'unlabeled data points from X that are drawn i.i.d. from PX' as part of its model, but does not refer to specific publicly available datasets or provide access information for empirical training data. |
| Dataset Splits | No | The paper is purely theoretical and does not involve empirical experiments or dataset evaluations, thus no validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not conduct empirical experiments, therefore no hardware specifications are mentioned. The reproducibility checklist states N/A for hardware. |
| Software Dependencies | No | The paper is theoretical and does not conduct empirical experiments, therefore no specific software dependencies with version numbers are mentioned. The reproducibility checklist states N/A for software. |
| Experiment Setup | No | The paper is purely theoretical and does not include empirical experiments, thus no experimental setup details like hyperparameters or training configurations are provided. |