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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Authors: Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama
ICML 2018 | Venue PDF | 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. |