Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Distributionally robust weighted k-nearest neighbors
Authors: Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, Yao Xie
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the competitive performance of our algorithm compared to the state-of-the-art in the few-training-sample setting with various real-data experiments.In this section, we evaluate our method and eight alternative approaches on four commonly-used image data sets: MNIST [28], CIFAR-10 [25], Omniglot [27], and present a set of comprehensive numerical examples. |
| Researcher Affiliation | Academia | Shixiang Zhu Carnegie Mellon University EMAIL; Liyan Xie The Chinese University of Hong Kong, Shenzhen EMAIL; Minghe Zhang Georgia Institute of Technology EMAIL; Rui Gao University of Texas at Austin EMAIL; Yao Xie Georgia Institute of Technology EMAIL |
| Pseudocode | Yes | The algorithm is summarized in Algorithm 1 (Appendix A).Appendix A contains 'Algorithm 1 Dr.k-NN'. |
| Open Source Code | No | The paper's ethics review states '[Yes]' for including code, data, and instructions, but it does not provide a direct URL to a source-code repository or an explicit statement within the main text that code is provided in supplementary material with specific access details. |
| Open Datasets | Yes | We evaluate our method and eight alternative approaches on four commonly-used image data sets: MNIST [28], CIFAR-10 [25], Omniglot [27]... We also test our method on two medical diagnosis data sets: Lung Cancer [12], and COVID-19 CT [44] |
| Dataset Splits | No | The paper states it uses M-class K-sample training tasks and tests with 1,000 unseen samples. It mentions hyper-parameter tuning by cross-validation but does not specify exact training/validation/test splits by percentages, sample counts, or specific cross-validation folds like '5-fold cross-validation'. |
| Hardware Specification | Yes | All experiments are performed on Google Colaboratory (Pro version) with 12GB RAM and dual-core Intel processors, which speed up to 2.3 GHz (without GPU). |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and a 'differentiable convex optimization layer' from a cited work [2], but it does not provide specific version numbers for these or other key software components like programming languages or libraries. |
| Experiment Setup | Yes | The Adam optimizer [23] is adopted for all experiments conducted in this paper, where learning rate is 10 2. The mini-batch size is 32... We use the Euclidean distance c( , 0) = k 0k2 throughout our experiment... we use the same network structure in matching network, prototypical network, and Meta Opt Net as we described above... single CNN layer... where the kernel size is 3, the stride is 1 and the width of the output layer is d = 400. |