Robust Correction of Sampling Bias using Cumulative Distribution Functions
Authors: Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Further, we show experimentally that our method is more robust in its predictions, is not reliant on parameter tuning and shows similar classification performance compared to the current state-of-the-art techniques on synthetic and real datasets. |
| Researcher Affiliation | Academia | Bijan Mazaheri Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125 bmazaher@caltech.edu Siddharth Jain Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, 91125 sidjain@caltech.edu Jehoshua Bruck Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, 91125 bruck@caltech.edu |
| Pseudocode | Yes | Algorithm 1 Covariate Shift Classification 1 [...] Algorithm 2 Covariate Shift Classification 2 |
| Open Source Code | No | The paper does not provide any links to source code for their proposed method, nor does it state that the code is available in supplementary materials or elsewhere. |
| Open Datasets | Yes | We conduct experiments on real datasets [18, 20, 9, 13, 21] and show comparable performance to other widely known covariate shift methods [11, 5, 14, 15, 6]. [...] twonorm and ringnorm datasets [9]. The cancer, diabetes and banknote datasets. |
| Dataset Splits | Yes | N = 200 training points and M = 1000 testing points were used. [...] A training set of size 100 is chosen uniformly at random from the remaining samples in the dataset. [...] The mean performance error over 100 trials for twonorm and ringnorm datasets [9] is provided in Table 1. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "matlab code on the authors website" for other methods but does not provide specific software dependencies with version numbers for their own implementation. |
| Experiment Setup | Yes | In Experiments 1 and 2 below, a SVM is used with a square rooted Gaussian kernel with kernel width = 1 and regularization coefficient γ = 0.1. |