Multitask Coactive Learning
Authors: Robby Goetschalckx, Alan Fern, Prasad Tadepalli
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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, tadepall@eecs.oregonstate.edu 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. |