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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bayesian Continuous-Time Tucker Decomposition
Authors: Shikai Fang, Akil Narayan, Robert Kirby, Shandian Zhe
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we examined our approach in both ablation study and real-world applications. On synthetic datasets, BCTT successful learned different temporal dynamics and recovered the clustering structures of the tensor nodes from their factor estimation. On three real-world temporal tensor datasets, BCTT significantly outperforms the competing dynamic decomposition methods, including discrete time factors and continuous time coefficients, often by a large margin. |
| Researcher Affiliation | Academia | 1School of Computing, University of Utah 2 Scientific Computing and Imaging (SCI) Institute, University of Utah 3Department of Mathematics, University of Utah. Correspondence to: Shandian Zhe <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 BCTT |
| Open Source Code | No | The paper mentions implementing our method BCTT with PyTorch (Paszke et al., 2019) but does not provide a link or explicit statement about releasing the source code for their method. |
| Open Datasets | Yes | (1) Movie Len100K (https://grouplens.org/datasets/movielens/)... (2) Ads Click (https://www.kaggle.com/ c/avazu-ctr-prediction)... (3) DBLP (https://dblp.uni-trier.de/ xml/)... |
| Dataset Splits | Yes | we randomly sampled 80% observed entry values and their time points for training, and then tested on the remaining entries. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or cloud instance specifications used for running experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch' but does not specify its version number or any other software dependencies with explicit version numbers. |
| Experiment Setup | Yes | We used the Mat ern kernel with ν = 3/2, and set l = σ2 = 0.1. We ran our message-passing inference until convergence. The tolerance level was set to 10 3. ... For CT-CP, we used 100 knots for the polynomial splines. Except BCTT, all the methods were trained with stochastic mini-batch optimization, with mini-batch size 100. We used ADAM optimization (Kingma and Ba, 2014). The learning rate was chosen from {10 4, 5 10 4, 10 3, 5 10 3, 10 2}. |