Sample Wise Certificate

Sample-wise certificates provide verifiable guarantees about the properties of individual data points or predictions within a larger system, addressing concerns about privacy, robustness, and fairness in machine learning models. Current research focuses on developing efficient algorithms, often based on convex relaxations, linear programming, or Fourier analysis, to generate these certificates for various applications, including differential privacy, graph neural networks, and federated learning. This work is significant because it moves beyond purely empirical evaluations, offering provable assurances and enhancing trust in the reliability and ethical implications of machine learning systems across diverse domains.