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Found 7 papers, 3 papers with code
Certifying Out-of-Domain Generalization for Blackbox Functions
As a result, the wider application of these techniques is currently limited by its scalability and flexibility -- these techniques often do not scale to large-scale datasets with modern deep neural networks or cannot handle loss functions which may be non-smooth such as the 0-1 loss.
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing
This link leads to a tight robustness condition which puts constraints on the amount of noise a classifier can tolerate, independent of whether the noise source is natural or adversarial.
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently.
Bayesian Deep Learning on a Quantum Computer
Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.
Smooth input preparation for quantum and quantum-inspired machine learning
Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage.
Quantum algorithms for training Gaussian Processes
Gaussian processes (GPs) are important models in supervised machine learning.
Quantum assisted Gaussian process regression
We show that even in some cases not ideally suited to the quantum linear systems algorithm, a polynomial increase in efficiency still occurs.
