Multitask learning meets tensor factorization: task imputation via convex optimization
Authors: Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both the theory and experiments support the advantage of the new norm when the tensor is not equal-sized and we do not a priori know which mode is low rank.We conducted several experiments to evaluate performances of tensor based multitask learning setting we have discussed in Section 3. In Section 4.1, we discuss simulation we conducted using synthetic data sets. In Sections 4.2 and 4.3, we discuss experiments on two real world data sets, namely the Restaurant data set [26] and School Effectiveness data set [3, 4]. |
| Researcher Affiliation | Academia | Kishan Wimalawarne Tokyo Institute of Technology Meguro-ku, Tokyo, Japan kishan@sg.cs.titech.ac.jp Masashi Sugiyama The University of Tokyo Bunkyo-ku, Tokyo, Japan sugi@k.u-tokyo.ac.jp Ryota Tomioka TTI-C Illinois, Chicago, USA tomioka@ttic.edu |
| Pseudocode | No | The paper includes mathematical formulations and theoretical analyses but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We discuss experiments on two real world data sets, namely the Restaurant data set [26] and School Effectiveness data set [3, 4]. |
| Dataset Splits | Yes | We also selected 250 instances as the validation set and the rest was used as the test set.selected the regularization parameter λ using two-fold cross validation on the training set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper describes mathematical formulations and computational methods but does not list any specific software dependencies or their version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | We used the penalty formulation of (6) with the squared loss and selected the regularization parameter λ using two-fold cross validation on the training set from the range 0.01 to 10 with the interval 0.1.The regularization parameter for each norm was selected by minimizing the mean squared error on the validation set from the candidate values in the interval [50, 1000] for the overlapped, [0.5, 40] for the latent, [6000, 20000] for the scaled latent norms, respectively. |