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.
Explores simplifying belief propagation equations for pairwise models, reducing computational complexity from order n cubed to order n.
Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
Explores decision-making under uncertainty, focusing on Kilian Schindler's posthumous PhD thesis on scalable stochastic optimization and scenario reduction.
Covers the Subset Sum problem and the efficient LLL algorithm for finding solutions in lattice basis reduction.
