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 [1].
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
Authors: Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami
NeurIPS 2016 | Venue PDF | 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 EMAIL, EMAIL 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.) |