Delves into challenges of learning from probabilistic models, covering computational complexity, data reconstruction, and statistical gaps.
Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
