The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning
cs.LG
Submitted: 2025-11-26
Updated: 2026-08-28
Terminology
Sources
- Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning
- Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation
- Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set
- Unique Rashomon Sets for Robust Active Learning
- Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization
- Perceptions of the Fairness Impacts of Multiplicity in Machine Learning
- Certified Adversarial Robustness with Additive Noise
- Countering Adversarial Images using Input Transformations
- Theoretical evidence for adversarial robustness through randomization
- Explaining and Harnessing Adversarial Examples
- Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
- On Detecting Adversarial Perturbations
- Do Wider Neural Networks Really Help Adversarial Robustness?
- Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
- Intriguing properties of neural networks
- Are adversarial examples inevitable?
- Robustness May Be at Odds with Accuracy
- Robustness Threats of Differential Privacy
- Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
- Robustness, Privacy, and Generalization of Adversarial Training
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