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..
Multitask Coactive Learning
Authors: Robby Goetschalckx, Alan Fern, Prasad Tadepalli
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments in three domains confirm that this algorithm is effective in the multitask setting, compared to natural baselines. |
| Researcher Affiliation | Academia | Robby Goetschalckx Alan Fern School of Computer Science Oregon State University Corvallis, OR 97330 goetschr, afern, EMAIL Prasad Tadepalli |
| Pseudocode | Yes | Algorithm 1 Multitask Coactive Learner (α, β) |
| Open Source Code | No | The paper does not provide any explicit statements or links for open-source code. |
| Open Datasets | Yes | The third domain is a real-world domain, namely the spam detection dataset as presented in the 2006 ECML/PKDD Discovery Challenge [Bickel, 2008], task b. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or references to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For each expert a user vector is generated by perturbing the base vector by a vector drawn from a normal distribution with mean 0 and diagonal covariance matrix σI10 and then normalizing. Experiments were performed with σ = 0.01, 0.05 and 0.25, resulting in values of δ of about 0.001, 0.02 and 0.38. In the experiment, the value κ = 0.1 was used. |