Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
Authors: Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,505 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,596 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Complementary to our theoretical results, our synthetic and real-world data evaluations showcase superior performance compared to related techniques. |
| Researcher Affiliation | Academia | 1 Computer Science Department, University of Southern California 2 Computer Science Department, Princeton University 3 Department of Electrical and Computer Engineering, University of Minnesota Twin Cities |
| Pseudocode | Yes | Algorithm 1 Tensor NOODL: Neurally plausible alternating Optimization-based Online Dictionary Learning for Tensor decompositions. Algorithm 2 Untangle Khatri-Rao Product (KRP): Recovering the Sparse factors |
| Open Source Code | Yes | Corresponding code is available at https://github.com/srambhatla/Tensor NOODL. |
| Open Datasets | No | The paper describes generating synthetic data and using specific real-world datasets (NBA Shot Pattern Dataset, Enron data) but does not provide specific links, DOIs, repositories, or formal citations for public access to these datasets as used in their experiments. |
| Dataset Splits | No | The paper describes synthetic data generation parameters and real-world data usage (weekly NBA data), but does not specify explicit train/validation/test dataset splits (percentages, sample counts, or citations to predefined splits) in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Experimental set-up: We compare Tensor NOODL with online dictionary learning algorithms presented in [19] (Arora(b) (incurs bias) and Arora(u) (claim no bias)), and [20], which can be viewed as a variant of ALS (matricized) 4. We analyze the recovery performance of the algorithms across different choices of tensor dimensions J = K = {100, 300, 500} for a fixed n = 300, rank m = {50, 150, 300, 450, 600}, and the sparsity parameters α = β = {0.005, 0.01, 0.05} of factors B (t) and C (t), across 3 Monte-Carlo runs 5. Parameters ηA, ηx, τ, T, C, and R as per A.3, A.5, and A.6. |