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].
Learning from Multiway Data: Simple and Efficient Tensor Regression
Authors: Rose Yu, Yan Liu
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate superior empirical performance on both multi-linear multi-task learning and spatio-temporal applications. |
| Researcher Affiliation | Academia | Rose Yu EMAIL Yan Liu EMAIL Department of Computer Science, University of Southern California |
| Pseudocode | Yes | Algorithm 1 Subsampled Tensor Projected Gradient; Algorithm 2 Iterative Tensor Projection (ITP) |
| Open Source Code | No | The paper does not provide any statements or links indicating that its source code is available. |
| Open Datasets | Yes | The U.S. Historical Climatology Network (USHCN) daily (http://cdiac.ornl. gov/ftp/ushcn_daily/) contains daily measurements for 5 climate variables for more than 100 years. |
| Dataset Splits | Yes | We also select 200 instances as the validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python version, specific library versions). |
| Experiment Setup | Yes | We choose VAR (3) model and use 5-fold cross-validation to select the rank during the training phase. For both datasets, we normalize each individual time series by removing the mean and dividing by standard deviation. The model parameters are selected by minimizing the mean squared error on the validation set. |