On Proper Learnability between Average- and Worst-case Robustness
Authors: Vinod Raman, UNIQUE SUBEDI, Ambuj Tewari
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study relaxations of the worst-case adversarially robust learning setup from a learning-theoretic standpoint. |
| Researcher Affiliation | Academia | Vinod Raman Department of Statistics University of Michigan Ann Arbor, MI 48104 vkraman@umich.edu Unique Subedi Department of Statistics University of Michigan Ann Arbor, MI 48104 subedi@umich.edu Ambuj Tewari Department of Statistics University of Michigan Ann Arbor, MI 48104 tewaria@umich.edu |
| Pseudocode | No | The paper is theoretical and does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. It refers to abstract concepts like 'distribution D over X Y' rather than named datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |