Modified Frank Wolfe in Probability Space
Authors: Carson Kent, Jiajin Li, Jose Blanchet, Peter W Glynn
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our algorithm to a range of functionals arising from applications in nonparametric estimation. All simulations are implemented using Python 3.8 on a high performance computing server running Ubuntu 18.04 with a Gen10 Quad Intel(R) Xeon(R) Platinum 8268 CPU @ 2.90GHz processor. |
| Researcher Affiliation | Academia | Carson Kent Stanford University crkent@stanford.edu Jose Blanchet Stanford University jose.blanchet@stanford.edu Jiajin Li The Chinese University of Hong Kong gerrili1996@gmail.com Peter Glynn Stanford University glynn@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Modified Frank Wolfe for (3) |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results? [Yes] See the supplemental material |
| Open Datasets | No | The paper mentions using a "2D Gaussian mixture" and "50 data points, Yi, sampled from a mixture of 7-Gaussians" and refers to "Student-Teacher Network problem" without providing concrete access information (link, DOI, repository, or formal citation with authors/year) for publicly available datasets. |
| Dataset Splits | No | The paper refers to a "validation dataset" in Figure 3 and states that "The detailed implementation set up is same as [3, Appendix G]" for the Student-Teacher Network problem. However, this paper does not explicitly provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) within its main text. |
| Hardware Specification | Yes | All simulations are implemented using Python 3.8 on a high performance computing server running Ubuntu 18.04 with a Gen10 Quad Intel(R) Xeon(R) Platinum 8268 CPU @ 2.90GHz processor. |
| Software Dependencies | Yes | All simulations are implemented using Python 3.8 |
| Experiment Setup | Yes | the number of particles is 200. The bisection method of Appendix C is used with tolerance set to 1e 3. For 50 data points, Yi, sampled from a mixture of 7-Gaussians, 200 particles are used in a non-parametric estimate the µi. the step size δ is 0.5. The number of particle is 200. In Figure 2(a), the tolerance for the ascent method is 1e 3 and the shaded bands show the standard derivation over 10 independent runs with random initializations. |