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].
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors
Authors: Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David Dunson, Lawrence Carin
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments with our model on both synthetic and real-world tensor datasets, and compare it with several baselines. The datasets used in our experiments span a wide range of application domains, such as chemometrics, multirelational social networks, brain-signal analysis (EEG), and image analysis. |
| Researcher Affiliation | Collaboration | Piyush Rai EMAIL Yingjian Wang EMAIL Shengbo Guo EMAIL Gary Chen EMAIL David Dunson EMAIL Lawrence Carin EMAIL Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA Samsung Research America Department of Statistical Science, Duke University, Durham, NC 27708, USA |
| Pseudocode | No | The paper describes the Gibbs sampling updates mathematically but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | Amino Acid data (Xu et al., 2013; Chu & Ghahramani, 2009), Flow Injection data (Xu et al., 2013; Chu & Ghahramani, 2009), Lazega-Lawyers multirelational social network data (Lazega, 2001), Kinship multirelational data (Nickel et al., 2011), Nation multirelational data (Nickel et al., 2011), benchmark Lena image. |
| Dataset Splits | Yes | For this task, we treat 50% of the data as missing and reconstruct it using the model learned on the remaining 50% data.For each dataset, except Kinship, we treat 90% of the entries as missing and predict them using the rest 10% data. For Kinship data we use the experimental setting of 90% training and 10% test data as done in other recent works (Nickel et al., 2011; Jenatton et al., 2012).Each experiment is repeated 10 times with different splits of observed and missing data. |
| Hardware Specification | No | The paper states it used an 'unoptimized MATLAB implementation' but does not provide any specific hardware details such as CPU, GPU, or memory specifications for running the experiments. |
| Software Dependencies | No | The paper mentions using an 'unoptimized MATLAB implementation' but does not specify any software versions or other dependencies. |
| Experiment Setup | Yes | For our model, we set ยต(k) r to zero vector and ฮฃ(k) r to identity matrix.We run the sampling based methods for 1500 iterations with 1000 burn-in iterations, collect samples every 5 iterations after the burnin phase, and report all results using the posterior sample based averages. |