Optimal Robust Learning of Discrete Distributions from Batches
Authors: Ayush Jain, Alon Orlitsky
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The algorithm s computational efficiency, enables the first experiments of learning with adversarial batches. We tested the algorithm on simulated data with various adversarialbatch distributions and adversarial noise levels up to β = 0.49. In Section 3 reports the performance of the algorithm on experiments performed on the simulated data. |
| Researcher Affiliation | Academia | 1University of California, San Diego. Correspondence to: Ayush Jain <ayjain@eng.ucsd.edu>. |
| Pseudocode | Yes | Algorithm 1 Batch Deletion; Algorithm 2 Robust Distribution Estimator |
| Open Source Code | Yes | We provide all codes and implementation details in the supplementary material. |
| Open Datasets | No | We evaluate the algorithm s performance on synthetic data. The paper mentions synthetic data but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for generating or obtaining this dataset. |
| Dataset Splits | No | The paper evaluates the algorithm on synthetic data and runs multiple trials, but it does not specify explicit training, validation, or test dataset splits, nor does it describe a cross-validation setup. |
| Hardware Specification | Yes | All experiments were performed on a laptop with a configuration of 2.3 GHz Intel Core i7 CPU and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions "We provide all codes and implementation details in the supplementary material," but it does not specify version numbers for any software dependencies, libraries, or solvers used in the implementation. |
| Experiment Setup | Yes | For the first plot we fix batch-size n = 1000 and β = 0.4 and vary alphabet size k. We generate m = k/(0.4)2 batches for each k. In the the second plot we fix β = 0.4 and k = 200 and vary batch size n. We choose m = 40 k β2 1000 n , this keeps the total number of samples n m, constant for different n. For the next plot we fix batch size n = 1000 and k = 200. |