Convex Basins in Single-Index Model Loss Landscapes: Applications to Robust Recovery under Strong Adversarial Corruption
arXiv:2605.29497v1 Announce Type: new Abstract: We study the problem of robustly learning Gaussian Single Index Models (SIMs) in the presence of heavy-tailed noise and a constant fraction of adversarially corrupted covariates and responses. Prior work on robust recovery has considered settings such as linear regression (Pensia et al., JASA 2024), strictly monotonic link functions (Awasthi et al., NeurIPS 2022), and phase retrieval (Buna and Rebeschini, AISTATS 2025). However, these techniques do not extend to generic asymmetric non-monotonic link functions such as \textsc{GeLU} and \textsc{Swi
This paper addresses limitations in current robust machine learning techniques by proposing solutions for more complex, non-monotonic link functions, relevant as AI models become more sophisticated and operate in varied, noisy environments.
A strategic reader should care because improving the robustness of AI models against adversarial corruption is crucial for their deployment in critical applications, ensuring reliability and trustworthiness.
This advancement means that machine learning models can be more reliably applied in scenarios with heavy data corruption, extending the practical scope for AI in complex and adversarial settings.
- · AI/ML researchers
- · Developers of robust AI systems
- · Sectors using AI in adversarial environments
- · Adversarial attackers relying on model fragility
- · Systems with high corruption vulnerability
Improved reliability and wider application of machine learning models in noisy or adversarial settings.
Increased trust in AI systems for critical functions, potentially accelerating AI adoption in sensitive industries.
The development of more resilient and adaptable AI agents capable of operating effectively in highly unpredictable real-world scenarios.
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Read at arXiv cs.LG