Approximate Profile Maximum Likelihood
Authors: Dmitri S. Pavlichin, Jiantao Jiao, Tsachy Weissman
JMLR 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively experiment with the approximate solution, and the empirical performance of our approach is competitive and sometimes significantly better than state-of-the-art performances for various estimation problems. Keywords: Profile maximum likelihood, dynamic programming, sufficient statistic, partition of multi-partite numbers, integer partition |
| Researcher Affiliation | Academia | Dmitri S. Pavlichin EMAIL Department of Applied Physics Stanford University Stanford, CA 94305, USA Jiantao Jiao EMAIL Department of Electrical Engineering and Computer Sciences University of California Berkeley, CA 94720, USA Tsachy Weissman EMAIL Department of Electrical Engineering Stanford University Stanford, CA 94305, USA |
| Pseudocode | Yes | Algorithm 1 Approximation to level set partition of the approximate D-dimensional PML distribution |
| Open Source Code | Yes | We provide code at https://github.com/dmitrip/PML and (Pavlichin et al., 2017) for computing the approximate PML distributions. |
| Open Datasets | No | The paper uses synthetic data generated from various distributions (Uniform, Mix 2 Uniforms, Zipf, Geometric) for its experiments. It does not explicitly mention the use of any publicly available or open datasets with specific access information (links, DOIs, formal citations). |
| Dataset Splits | No | The paper does not provide explicit details about dataset splits (e.g., train/test/validation percentages or counts). The experiments involve drawing 'n' samples from a distribution for estimation tasks, which does not typically involve predefined dataset splits in the conventional machine learning sense. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing platforms used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for implementing or running the experiments. |
| Experiment Setup | Yes | In all cases K = |X| = 104. Uniform is uniform on X, Mix 2 Uniforms is a mixture of two uniform distributions, with half the probability mass on the first K/5 symbols, and the other half on the remaining symbols, and Zipf(α) 1/iα with i {1, . . . , K}. MLE denotes the ML plugin (naive) approach of using the sorted empirical distribution in (61). VV is (Valiant and Valiant, 2013). Each data point represents 100 random trials, with 2 standard error bars smaller than the plot marker for most points. |