Dickerson, John P.

Adversarial training for free

Shafahi, A., Najibi, M., Ghiasi, A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., & Goldstein, T. (2019) · ETH Zurich

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 580

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 267

Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation

Kumar, I. E., Hines, K. E., & Dickerson, J. P. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIFinancial Distress and Bankruptcy Prediction

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Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking

Feuer, B., Goldblum, M., Datta, T., Nambiar, S., Besaleli, R., Dooley, S., Cembalest, M., & Dickerson, J. P. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2