Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
Authors: Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Numerical experiments We numerically compare the following methods in multitask learning problems (Eq. (2)): ... 7.1 Restaurant data ... Fig. 1 shows the relative MSE ... Fig. 2 shows the performance comparison ... 7.2 Online shopping data ... The result is shown in Fig. 3, which presents the validation error (MSE) against the size of the training data. |
| Researcher Affiliation | Collaboration | Taiji Suzuki , Heishiro Kanagawa , Department of Mathematical and Computing Science, Tokyo Institute of Technology PRESTO, Japan Science and Technology Agency Center for Advanced Integrated Intelligence Research, RIKEN s-taiji@is.titech.ac.jp, kanagawa.h.ab@m.titech.ac.jp Hayato Kobayash, Nobuyuki Shimizu, Yukihiro Tagami Yahoo Japan Corporation { hakobaya, nobushim, yutagami } @yahoo-corp.jp |
| Pseudocode | Yes | Algorithm 1 Alternating minimization procedure for nonlinear tensor estimation |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available or provide links to a code repository for the described methodology. |
| Open Datasets | Yes | Here, we compared the methods in the Restaurant & Consumer Dataset [7]. ... [7] V.-G. Blanca, G.-S. Gabriel, and P.-M. Rafael. Effects of relevant contextual features in the performance of a restaurant recommender system. In Proceedings of 3rd Workshop on Context-Aware Recommender Systems, 2011. |
| Dataset Splits | No | Fig. 1 shows the relative MSE (the discrepancy of MSE from the best one) for different training sample sizes n computed on the validation data against the number of iterations t averaged over 10 repetitions. ... The kernel width and the regularization parameter were tuned by cross validation. (The paper mentions 'validation data' and 'cross-validation' but does not provide specific dataset split percentages, sample counts, or detailed splitting methodology needed for full reproduction.) |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Gaussian process method (GP-MTL)' and 'scikit-learn package' (in related works reference), but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | No | The kernel width and the regularization parameter were tuned by cross validation. (This indicates a tuning strategy but does not provide specific hyperparameter values or detailed training configurations such as learning rates, batch sizes, etc.) |