Class-Weighted Classification: Trade-offs and Robust Approaches
Authors: Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate the efficacy of LCVa R and LHCVa R on improving class conditional risks. |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University, Pennsylvania, United States 2Computer Science Department, Carnegie Mellon University, Pennsylvania, United States. |
| Pseudocode | No | No explicitly labeled pseudocode or algorithm block is present in the main text provided. The paper mentions pseudocode might be in the appendix, which is not available. |
| Open Source Code | Yes | Code for reproducing the results in this section can be found at https://www.github.com/neilzxu/ robust_weighted_classification. |
| Open Datasets | Yes | Real World Datasets We also experiment on the Covertype dataset taken from the UCI dataset repository (Dua & Graff, 2017). |
| Dataset Splits | No | The paper mentions '10,000 data points for both train and test sets' for synthetic data and a 'train-test split' for Covertype, but does not specify a validation set or explicit train/validation/test split percentages. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions training a logistic regression model with gradient descent on a cross entropy loss, but does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | LCVa R The empirical formulation optimizes the dual formulation, in which α is a hyperparameter: \LCVa Rα(f) = min λ R i=1 bpi( b Ri(f) λ)+ + λ. To reduce the number of hyperparameters to only c (0, 1] and κ (0, ), we calculate αi as follows: α(κ,c) i = c bpi 1/κ Pk j=1 bpj 1/κ. We train a logistic regression model with gradient descent on a cross entropy loss, which acts as a convex surrogate loss for zero-one risk. |