arXiv:2607.07671v1 Announce Type: new Abstract: Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However, standard likelihood-based PC learning is vulnerable to overfitting and fragile generalization when confronted with data noise, small sample sizes, or distribution shifts. This can be mitigated using distributionally-robust optimization which consider worst-case distributions within a Wasserstein ball of the empirical distribution, but current methods are limited to training a model from scratch in
The increasing deployment of AI models in critical applications highlights the urgent need for robust generalization capabilities, particularly in the face of noisy or shifted data distributions.
Improving the robustness of probabilistic circuits allows for more reliable and generalizable AI systems, reducing risks associated with real-world data variability and adversarial attacks.
This research introduces a post-training method to enhance the robustness of probabilistic circuits, offering an alternative to computationally intensive robust training from scratch.
- · AI developers
- · Industries relying on AI for critical decision-making
- · Machine learning researchers
- · AI systems vulnerable to data noise
- · Less robust AI models
AI models become more resilient to real-world data imperfections, leading to increased trust and broader deployment.
The reduced need for retraining models from scratch on new data improves efficiency and reduces computational costs in AI development cycles.
More robust and reliable AI systems may accelerate the adoption of autonomous decision-making in high-stakes environments, potentially impacting regulatory frameworks.
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Read at arXiv cs.LG