Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Authors: Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Motivated by our analysis, we propose simple DRSL that overcomes this pessimism and empirically demonstrate its effectiveness. Finally, we demonstrate the effectiveness of our DRSL through experiments (Section 6). |
| Researcher Affiliation | Academia | 1University of Tokyo, Japan 2RIKEN, Tokyo, Japan. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for source code. |
| Open Datasets | Yes | We obtained six classification datasets from the UCI repository (Blake & Merz, 1998), two of which are for multi-class classification. We also obtained MNIST (Le Cun et al., 1998) and 20newsgroups (Lang, 1995). |
| Dataset Splits | Yes | The regularization hyper-parameter λ was selected from {1.0, 0.1, 0.01, 0.001, 0.0001} via 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions general software components like 'linear models with softmax output' and 'cross-entropy loss', but does not specify any software names with version numbers. |
| Experiment Setup | Yes | For all the methods, we used linear models with softmax output for the prediction function gθ(x). The cross-entropy loss with ℓ2 regularization was adopted. The regularization hyper-parameter λ was selected from {1.0, 0.1, 0.01, 0.001, 0.0001} via 5-fold cross validation. We used the two f-divergences (the KL and PE divergences) and set δ = 0.5 for AERM and structural AERM. |