Improper Learning

Improper learning, where the learned model doesn't necessarily belong to the same class as the target function, is a significant area of machine learning research. Current investigations focus on understanding the computational and statistical trade-offs between proper and improper learning in various contexts, including sparse linear regression and quantum learning settings, exploring the sample complexity needed for efficient learning and analyzing the impact of improper learning on model robustness and security. These studies are crucial for developing more efficient and resilient machine learning algorithms, particularly in high-dimensional data scenarios and for mitigating vulnerabilities like Trojan attacks in knowledge distillation.