A General Framework for Robust Interactive Learning
Authors: Ehsan Emamjomeh-Zadeh, David Kempe
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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, emamjome@usc.edu Department of Computer Science, University of Southern California, dkempe@usc.edu |
| 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. |