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..
A General Framework for Robust Interactive Learning
Authors: Ehsan Emamjomeh-Zadeh, David Kempe
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a general framework for interactively learning models, such as (binary or non-binary) classifiers, orderings/rankings of items, or clusterings of data points. Our framework is based on a generalization of Angluin s equivalence query model and Littlestone s online learning model |
| Researcher Affiliation | Academia | Department of Computer Science, University of Southern California, EMAIL Department of Computer Science, University of Southern California, EMAIL |
| Pseudocode | Yes | Algorithm 1 LEARNING A MODEL WITHOUT FEEDBACK ERRORS (Sinit) |
| Open Source Code | No | The paper mentions a full version on arXiv, but does not provide any statement or link for the release of source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical framework and its applications but does not detail the use of any specific publicly available datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper focuses on theoretical aspects and does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training configurations. |