insight - Machine Learning - # Adversarially Robust Conformal Prediction

Verifiably Robust Conformal Prediction for Improved Uncertainty Quantification Under Adversarial Attacks

Q: How might the computational cost of VRCP be further optimized for deployment in real-time systems that require fast uncertainty quantification?

VRCP's reliance on neural network verification, while offering strong guarantees, can be computationally demanding, especially for real-time applications. Here are some potential optimization strategies: Faster Verification Algorithms: The most direct approach is to leverage advancements in NN verification research. Utilizing faster, potentially parallel or GPU-accelerated, verification algorithms, particularly incomplete but sound verifiers with tight bounds, can significantly reduce verification time. Approximation Techniques: Trading off some degree of robustness for speed is possible. Approximate verification methods or using less precise but faster bounds from existing verifiers could be explored. Selective Verification: Instead of verifying every input, a more targeted approach could be adopted. For instance: Importance-based Verification: Focus verification efforts on inputs identified as more critical or uncertain based on a preliminary analysis or uncertainty scores. Adaptive Verification: Dynamically adjust the verification effort based on the input. Simple inputs might not require full verification, while more complex ones could trigger more rigorous analysis. Pre-computed Bounds: For VRCP-C, where verification happens during calibration, pre-computing and storing bounds for a representative set of inputs could speed up inference. This would involve a trade-off between storage space and online computation. Model Distillation: Employing knowledge distillation techniques to transfer robustness from a larger, verified model to a smaller, faster model could be beneficial. The smaller model could then be used for faster uncertainty quantification. It's important to note that the optimal optimization strategy would depend on the specific application, the desired level of robustness, and the available computational resources.

Q: Could the use of adversarial training during model development negate the need for robust conformal prediction methods like VRCP, or would these approaches still offer complementary benefits?

While adversarial training can enhance a model's robustness to adversarial attacks, it doesn't negate the need for robust conformal prediction methods like VRCP. Here's why: Complementary Benefits: Adversarial training and VRCP offer distinct advantages: Adversarial Training: Improves the model's inherent robustness by incorporating adversarial examples during training, leading to better point predictions under attack. VRCP: Provides statistically valid prediction sets, quantifying uncertainty in the presence of adversarial perturbations, even for unseen attacks. Unknown Attack Budget: Adversarial training typically assumes a known attack budget (epsilon). VRCP, on the other hand, can handle varying or unknown attack budgets at inference time, offering greater flexibility. Beyond Point Predictions: Adversarial training primarily focuses on improving the accuracy of point predictions. VRCP goes beyond point estimates by providing calibrated uncertainty estimates, which are crucial for risk-sensitive applications. Distribution Shifts: While adversarial training improves robustness to a specific type of distribution shift (adversarial perturbations), VRCP's distribution-free nature makes it more resilient to other unforeseen distribution shifts that might occur in real-world deployments. In essence, adversarial training and VRCP are complementary techniques. Adversarial training can be seen as a method to improve the underlying model's robustness, while VRCP provides a principled framework for quantifying uncertainty in the presence of adversarial examples, even for adversarially trained models.

Q: How can the principles of verifiably robust uncertainty quantification be extended beyond traditional machine learning models to address the challenges of explainability and trustworthiness in the context of increasingly complex AI systems?

Extending verifiably robust uncertainty quantification beyond traditional models is crucial for building trustworthy AI. Here are some potential directions: Beyond Neural Networks: While VRCP focuses on neural networks, extending similar principles to other models like tree-based ensembles, Bayesian models, or even symbolic AI systems is essential. This might involve developing new verification techniques or adapting existing ones. Explainable Verification: Current verification methods often provide binary answers (robust or not). Developing more explainable verification techniques that highlight which parts of the input or model contribute to the uncertainty or robustness could enhance trust and understanding. Compositional Verification: As AI systems become more complex, involving multiple interacting components, compositional verification techniques that provide guarantees for the entire system by analyzing individual components and their interactions become crucial. Human-in-the-Loop Verification: Integrating human expertise into the verification process can be beneficial. This could involve using human knowledge to guide the verification process, provide insights into potential vulnerabilities, or validate the results of automated verification. Uncertainty-Aware Decision Making: Developing decision-making frameworks that explicitly account for the quantified uncertainty provided by methods like VRCP is essential. This ensures that decisions are made with an appropriate level of caution, especially in high-stakes scenarios. Addressing these challenges requires a multidisciplinary effort involving machine learning researchers, formal verification experts, domain experts, and ethicists. By integrating verifiably robust uncertainty quantification into the design and deployment of complex AI systems, we can move towards more explainable, trustworthy, and reliable AI.

Core Concepts

This paper introduces VRCP, a novel framework that leverages conformal prediction and neural network verification to construct prediction sets that maintain coverage guarantees for machine learning models, even in the presence of adversarial attacks.

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arxiv.org

Jeary, L., Kuipers, T., Hosseini, M., & Paoletti, N. (2024). Verifiably Robust Conformal Prediction. Advances in Neural Information Processing Systems, 38.

This paper addresses the vulnerability of Conformal Prediction (CP) methods to adversarial attacks, which can significantly reduce the coverage guarantees of predicted sets. The authors aim to develop a new framework called Verifiably Robust Conformal Prediction (VRCP) that leverages neural network verification techniques to provide robust and efficient prediction sets even under adversarial perturbations.

Key Insights Distilled From

Verifiably Robust Conformal Prediction

by Linus Jeary,... at arxiv.org 11-19-2024

https://arxiv.org/pdf/2405.18942.pdf

Deeper Inquiries

How might the computational cost of VRCP be further optimized for deployment in real-time systems that require fast uncertainty quantification?

VRCP's reliance on neural network verification, while offering strong guarantees, can be computationally demanding, especially for real-time applications. Here are some potential optimization strategies:

Faster Verification Algorithms: The most direct approach is to leverage advancements in NN verification research. Utilizing faster, potentially parallel or GPU-accelerated, verification algorithms, particularly incomplete but sound verifiers with tight bounds, can significantly reduce verification time.
Approximation Techniques:  Trading off some degree of robustness for speed is possible. Approximate verification methods or using less precise but faster bounds from existing verifiers could be explored.
Selective Verification:  Instead of verifying every input, a more targeted approach could be adopted. For instance:

Importance-based Verification:  Focus verification efforts on inputs identified as more critical or uncertain based on a preliminary analysis or uncertainty scores.
Adaptive Verification:  Dynamically adjust the verification effort based on the input. Simple inputs might not require full verification, while more complex ones could trigger more rigorous analysis.


Pre-computed Bounds: For VRCP-C, where verification happens during calibration, pre-computing and storing bounds for a representative set of inputs could speed up inference. This would involve a trade-off between storage space and online computation.
Model Distillation: Employing knowledge distillation techniques to transfer robustness from a larger, verified model to a smaller, faster model could be beneficial. The smaller model could then be used for faster uncertainty quantification.
It's important to note that the optimal optimization strategy would depend on the specific application, the desired level of robustness, and the available computational resources.

Could the use of adversarial training during model development negate the need for robust conformal prediction methods like VRCP, or would these approaches still offer complementary benefits?

While adversarial training can enhance a model's robustness to adversarial attacks, it doesn't negate the need for robust conformal prediction methods like VRCP. Here's why:

Complementary Benefits: Adversarial training and VRCP offer distinct advantages:

Adversarial Training: Improves the model's inherent robustness by incorporating adversarial examples during training, leading to better point predictions under attack.
VRCP: Provides statistically valid prediction sets, quantifying uncertainty in the presence of adversarial perturbations, even for unseen attacks.


Unknown Attack Budget: Adversarial training typically assumes a known attack budget (epsilon). VRCP, on the other hand, can handle varying or unknown attack budgets at inference time, offering greater flexibility.
Beyond Point Predictions: Adversarial training primarily focuses on improving the accuracy of point predictions. VRCP goes beyond point estimates by providing calibrated uncertainty estimates, which are crucial for risk-sensitive applications.
Distribution Shifts: While adversarial training improves robustness to a specific type of distribution shift (adversarial perturbations), VRCP's distribution-free nature makes it more resilient to other unforeseen distribution shifts that might occur in real-world deployments.
In essence, adversarial training and VRCP are complementary techniques. Adversarial training can be seen as a method to improve the underlying model's robustness, while VRCP provides a principled framework for quantifying uncertainty in the presence of adversarial examples, even for adversarially trained models.

How can the principles of verifiably robust uncertainty quantification be extended beyond traditional machine learning models to address the challenges of explainability and trustworthiness in the context of increasingly complex AI systems?

Extending verifiably robust uncertainty quantification beyond traditional models is crucial for building trustworthy AI. Here are some potential directions:

Beyond Neural Networks:  While VRCP focuses on neural networks, extending similar principles to other models like tree-based ensembles, Bayesian models, or even symbolic AI systems is essential. This might involve developing new verification techniques or adapting existing ones.
Explainable Verification:  Current verification methods often provide binary answers (robust or not). Developing more explainable verification techniques that highlight which parts of the input or model contribute to the uncertainty or robustness could enhance trust and understanding.
Compositional Verification:  As AI systems become more complex, involving multiple interacting components, compositional verification techniques that provide guarantees for the entire system by analyzing individual components and their interactions become crucial.
Human-in-the-Loop Verification:  Integrating human expertise into the verification process can be beneficial. This could involve using human knowledge to guide the verification process, provide insights into potential vulnerabilities, or validate the results of automated verification.
Uncertainty-Aware Decision Making:  Developing decision-making frameworks that explicitly account for the quantified uncertainty provided by methods like VRCP is essential. This ensures that decisions are made with an appropriate level of caution, especially in high-stakes scenarios.
Addressing these challenges requires a multidisciplinary effort involving machine learning researchers, formal verification experts, domain experts, and ethicists. By integrating verifiably robust uncertainty quantification into the design and deployment of complex AI systems, we can move towards more explainable, trustworthy, and reliable AI.