Active Learning from Peers
Authors: Keerthiram Murugesan, Jaime Carbonell
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments over three multitask learning benchmark datasets show clearly superior performance over baselines such as assuming task independence, learning only from the oracle and not learning from peer tasks. |
| Researcher Affiliation | Academia | Keerthiram Murugesan Jaime Carbonell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 {kmuruges,jgc}@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1: Active Learning from Peers |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the described methodology. |
| Open Datasets | Yes | Landmine Detection3 consists of 19 tasks collected from different landmine fields. 3http://www.ee.duke.edu/~lcarin/Landmine Data.zip. Spam Detection4 We use the dataset obtained from ECML PAKDD 2006 Discovery challenge for the spam detection task. 4http://ecmlpkdd2006.org/challenge.html. Sentiment Analysis5 We evaluated our algorithm on product reviews from Amazon on a dataset containing reviews from 24 domains. 5http://www.cs.jhu.edu/~mdredze/datasets/sentiment |
| Dataset Splits | Yes | Unless otherwise specified, all model parameters are chosen via 5-fold cross validation. |
| Hardware Specification | No | The paper mentions 'CPU time' but does not specify any particular hardware components such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We set the value of b1 = 1 for all the experiments and the value of b2 is tuned from 20 different values. Unless otherwise specified, all model parameters are chosen via 5-fold cross validation. In order to efficiently evaluate the proposed methods, we restrict the total number of label requests issued to the oracle during training, that is we give all the methods the same query budget: (10%, 20%, 30%) of the total number of examples T on sentiment dataset. |