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].
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space
Authors: Shuo Yang, Yanyao Shen, Sujay Sanghavi
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate its value via synthetic experiments. Moreover, we numerically show that Int HT can be extended to higher-order regression problems, and also theoretically analyze an SVRG variant of Int HT. ... 5 Synthetic Experiments To examine the sub-quadratic time and space complexity, we design three tasks to answer the following three questions: |
| Researcher Affiliation | Academia | Shuo Yang Department of Computer Science University of Texas at Austin Austin, TX 78712 EMAIL Yanyao Shen ECE Department University of Texas at Austin Austin, TX 78712 EMAIL Sujay Sanghavi ECE Department University of Texas at Austin Austin, TX 78712 EMAIL |
| Pseudocode | Yes | Algorithm 1 INTERACTION HARD THRESHOLDING (INTHT) ... Algorithm 2 APPROXIMATED TOP ELEMENTS EXTRACTION (ATEE) |
| Open Source Code | No | The paper does not provide any links to open-source code or state that the code for their method is publicly available. |
| Open Datasets | No | The paper states: "We generate feature vectors xi, whose coordinates follow i.i.d. uniform distribution on [−1, 1]. ... The output yis, are generated following x⊤i Θ xi." This indicates the use of synthetically generated data, not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper uses synthetically generated data and mentions experiments are "averaged over 3 independent runs" or "averaged over 5 independent runs," but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies, libraries, or their version numbers used for the implementation or experiments. |
| Experiment Setup | Yes | Experimental setting ... by default, we set p = 200, d = 3, K = 20, k = 3K, η = 0.2. Support recovery results with different b-K combinations are averaged over 3 independent runs, results for m-p combinations are averaged over 5 independent runs. All experiments are terminated after 150 iterations. |