ESE Ph.D. Thesis Defense: “Statistical Limits and Efficient Algorithms for Learning-Enabled Control”
ESE Ph.D. Thesis Defense: “Statistical Limits and Efficient Algorithms for Learning-Enabled Control”
As the adoption of large-scale learning for control continues to grow, developing sample-efficient algorithms has become critical. Yet, even in simple settings, algorithms achieving optimal sample complexity for specific problem instances often remain unknown. Motivated by this limitation, we discuss recent progress toward understanding sample-efficient methods in learning-enabled control. We first examine the statistical limits […]