Consistent Multitask Learning with Nonlinear Output Relations
Authors: Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Encouraging experimental results show the benefits of the proposed method in practice. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University College London, London, UK. 2INRIA Sierra Project-team and École Normale Supérieure, Paris, France. 3Massachusetts Institute of Technology, Cambridge, USA. 4Università degli studi di Genova, Genova, Italy. 5Istituto Italiano di Tecnologia, Genova, Italy. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Sarcos Dataset. We report experiments on the Sarcos dataset [22]. We performed experiments on Movielens100k [40] (movies = documents, users = queries) |
| Dataset Splits | Yes | We used a Gaussian kernel on the input and chose the corresponding bandwidth and the regularization parameter λ by hold-out crossvalidation on 30% of the training set (see details in the appendix). We used the 10 dataset splits available online for the dataset in [13], each containing 2000 examples per task with 15 examples used for training/validation while the rest is used to measure errors in terms of the explained variance. We used the (linear) input kernel and the train, validation and test splits adopted in [21] to perform 10 independent trials with 5-fold cross-validation for model selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions that hyperparameters like bandwidth and regularization parameter were chosen by cross-validation but does not state their specific values or other concrete training configurations. |