Inverse Covariance Estimation with Structured Groups
Authors: Shaozhe Tao, Yifan Sun, Daniel Boley
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results show significant improvement in sample complexity when the correct group structure is known. We also apply these estimators to 14,910 stock closing prices, with noticeable improvement when group sparsity is exploited. |
| Researcher Affiliation | Collaboration | Shaozhe Tao Yifan Sun Daniel Boley University of Minnesota Technicolor Research University of Minnesota taoxx120@umn.edu yifan.sun@technicolor.com boley@cs.umn.edu |
| Pseudocode | Yes | Algorithm 1 One step of Frank-Wolfe algorithm; Algorithm 2 One step of Frank-Wolfe algorithm for (4) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Using the Yahoo! ticker downloader [4] we downloaded 27684 tickers for different stocks. We then used the Yahoo! finance API [5] to gather daily open, high, low, close, volume, and adjusted closing prices. [4] https://pypi.python.org/pypi/ Yahoo-ticker-downloader [5] http://chart.finance.yahoo.com/table.csv?s=TICKERNAMEHERE&a=400&b=23&c=2016&d=0&e=23&f=2017&g=d&ignore=.csv |
| Dataset Splits | Yes | Define V = {1, 2, . . . , 100}, T = {101, 102, . . . , 200}, and R = {201, 202, . . . , 200 + n} as the indices of a validation, test, and train set respectively |
| Hardware Specification | No | The paper does not provide specific details on the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like 'Yahoo! ticker downloader' and 'Yahoo! finance API' used for data scraping, but does not provide specific version numbers for these or any other software dependencies crucial for reproducibility. |
| Experiment Setup | Yes | To pick α and ρ, we swept powers of two in {2 5, . . . , 25} and picked the best performing ρ or α for each test. In cases where the best performing ρ or α was on the boundary, additional parameters were tested until this was no longer the case. |