Distributionally Robust Local Non-parametric Conditional Estimation
Authors: Viet Anh Nguyen, Fan Zhang, Jose Blanchet, Erick Delage, Yinyu Ye
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with synthetic and MNIST datasets show the competitive performance of this new class of estimators. |
| Researcher Affiliation | Academia | Viet Anh Nguyen Fan Zhang José Blanchet Stanford University, United States {viet-anh.nguyen, fzh, jose.blanchet}@stanford.edu Erick Delage HEC Montréal, Canada erick.delage@hec.ca Yinyu Ye Stanford University, United States yinyu-ye@stanford.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/nvietanh/DRCME. |
| Open Datasets | Yes | In this section we compare the quality of our proposed Distributionally Robust Conditional Mean Estimator (DRCME) to k-nearest neighbour (k-NN), Nadaraya-Watson (N-W), and Nadaraya Epanechnikov (N-E) estimators, together with the robust k-NN approach in [2] (Bert Et Al) using a synthetic and the MNIST datasets.using the MNIST database [23]. |
| Dataset Splits | Yes | The hyperparameters of all the estimators, whose range and selection are given in Appendix A, are chosen by leave-one-out cross validation. In each experiment, the hyper-parameters of all four methods were chosen based on a leave-one-out cross validation process. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions "commercial optimization solvers such as MOSEK [27]" and "Python Optimal Transport toolbox [12]" but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The hyperparameters of all the estimators, whose range and selection are given in Appendix A, are chosen by leave-one-out cross validation. Table 1 presents the median choice of hyper parameters for each estimator. |