arXiv:2601.02193v2 Announce Type: replace Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversarial corruption model. In this model, an adversary, upon looking at a "clean" i.i.d. dataset, inserts additional "corrupted" points of their choice into the dataset. These added points are constrained to be monotone corruptions, in that they get labeled according to the ground-truth target function. Perhaps surprisingly, we demonstrate that in this setting, all known optimal learning algorithms fo
This research is a continuation of ongoing efforts to understand and improve the robustness and reliability of machine learning algorithms in the face of various forms of data corruption and adversarial attacks.
A strategic reader should care because data integrity and model robustness are critical for the deployment of reliable AI systems in sensitive applications, impacting trust and adoption.
This paper highlights fundamental limitations of current optimal learning algorithms under a specific, yet potentially common, type of adversarial data corruption, suggesting a need for reconsidering assumptions in algorithm design.
- · AI robustness researchers
- · Security-focused AI developers
- · Industries with adversarial concerns
- · Developers relying solely on current optimal learning algorithms
- · Applications with unmitigated data input risks
Existing machine learning models may be more vulnerable to certain forms of data corruption than previously understood.
New research and development efforts will likely focus on designing algorithms specifically resilient to monotone adversarial corruptions.
The findings could drive a re-evaluation of data collection and validation protocols in critical AI applications, potentially increasing development costs and timelines.
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