The Many Faces of Adversarial Risk
Authors: Muni Sreenivas Pydi, Varun Jog
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our technical tools derive from optimal transport, robust statistics, functional analysis, and game theory. Our contributions include the following: generalizing Strassen s theorem to the unbalanced optimal transport setting with applications to adversarial classification with unequal priors; showing an equivalence between adversarial robustness and robust hypothesis testing with -Wasserstein uncertainty sets; proving the existence of a pure Nash equilibrium in the two-player game between the adversary and the algorithm; and characterizing adversarial risk by the minimum Bayes error between a pair of distributions belonging to the -Wasserstein uncertainty sets. Our results generalize and deepen recently discovered connections between optimal transport and adversarial robustness and reveal new connections to Choquet capacities and game theory. |
| Researcher Affiliation | Academia | Muni Sreenivas Pydi University of Wisconsin Madison Madison, Wisconsin, USA pydi@wisc.edu Varun Jog University of Cambridge Cambridge, UK vj270@cam.ac.uk |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training or evaluation on datasets. It focuses on mathematical definitions and proofs related to adversarial risk and optimal transport. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data splits for validation or training. It discusses mathematical concepts of distributions (p0, p1) but not actual datasets. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any 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. |