Provably Efficient Learning

Provably efficient learning aims to develop machine learning algorithms with guaranteed performance bounds, moving beyond empirical evaluations to establish theoretical guarantees of convergence and optimality. Current research focuses on improving the efficiency of existing algorithms like Q-learning through techniques such as target networks and experience replay, and extending their applicability to complex settings involving higher-order interactions (e.g., tensor attention) and nonlinear systems (e.g., nonlinear tomography). This rigorous approach is crucial for building reliable and trustworthy AI systems, particularly in safety-critical applications where performance guarantees are essential.