Robust Bayesian Classification Using An Optimistic Score Ratio
Authors: Viet Anh Nguyen, Nian Si, Jose Blanchet
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase the power of the proposed optimistic score ratio classifier on both synthetic and empirical data. |
| Researcher Affiliation | Academia | 1Stanford University. Correspondence to: Viet Anh Nguyen <viet-anh.nguyen@stanford.edu>. |
| Pseudocode | Yes | Algorithm 1 Optimistic score ratio classification |
| Open Source Code | Yes | All experiments are run on a standard laptop with 1.4 GHz Intel Core i5 and 8GB of memory, the codes and datasets are available at https://github.com/nian-si/bsc. |
| Open Datasets | Yes | We test the performance of our classification rules on various datasets from the UCI repository (Dua & Graff, 2017). and the reference: Dua, D. and Graff, C. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | for all methods that need cross-validation, we randomly select 75% of the data for training and the remaining 25% for testing. The size of the ambiguity sets and the regularization parameter are selected using stratified 5-fold cross-validation. |
| Hardware Specification | Yes | All experiments are run on a standard laptop with 1.4 GHz Intel Core i5 and 8GB of memory |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | We tune the threshold to maximize the training accuracy following (13) after computing the ratio value for each training sample. and for the second criteria, we choose ρc = n 1 c χ2 α(d(d + 3)/2) c {0, 1}, where nc is the number of training samples in class c and χ2 α(d(d + 3)/2) is the α-quantile of the chi-square distribution with d(d + 3)/2 degrees of freedom. ... so we select numerically α = 0.5 in our experiments. |