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..
Black-box Certification and Learning under Adversarial Perturbations
Authors: Hassan Ashtiani, Vinayak Pathak, Ruth Urner
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formally study the problem of classification under adversarial perturbations from a learner s perspective as well as a third-party who aims at certifying the robustness of a given black-box classifier. We analyze a PAC-type framework of semisupervised learning and identify possibility and impossibility results for proper learning of VCclasses in this setting. We further introduce a new setting of black-box certification under limited query budget, and analyze this for various classes of predictors and perturbation. |
| Researcher Affiliation | Collaboration | 1Department of Computing and Software, Mc Master University, Hamilton, ON, Canada 2Scotiabank, Toronto, ON, Canada 3Lassonde School of Engineering, EECS Department, York University, Toronto, ON, Canada. |
| Pseudocode | No | The paper contains formal definitions, theorems, and proof sketches, but no sections or figures labeled 'Pseudocode' or 'Algorithm', nor any structured, code-like steps for a procedure. |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available, nor does it include links to a code repository. |
| Open Datasets | No | This is a theoretical paper and does not describe the use of any datasets for training or experimentation. |
| Dataset Splits | No | This paper is theoretical and does not describe experimental validation using dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not describe any experimental setup involving specific hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical analysis and does not describe any specific software dependencies or versions used for implementation or experimentation. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe any experimental setup with specific hyperparameters, training configurations, or system-level settings. |