Private and interpretable clinical prediction with quantum-inspired tensor train models
cs.LG, cs.CR, quant-ph
Submitted: 2026-02-05
Updated: 2026-08-27
Code: https://github.com/joserapa98/tns4loris
Terminology
Sources
- Deep Learning with Differential Privacy
- Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
- Reconstructing Training Data with Informed Adversaries
- Differentially Private Empirical Risk Minimization
- Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems
- Issues Encountered Deploying Differential Privacy
- MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
- A Matrix Product State Model for Simultaneous Classification and Generation
- Tensorizing Neural Networks
- Exponential Machines
- A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
- Reconstructing Training Data From Real World Models Trained with Transfer Learning
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
- TensorKrowch: Smooth integration of tensor networks in machine learning
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Scikit-learn: Machine Learning in Python
- Privacy-preserving machine learning with tensor networks
- Matrix Product State Representations
- Membership Privacy for Machine Learning Models Through Knowledge Transfer
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