Explore a theory seminar on the smoothed analysis of the greedy algorithm in bandit learning. Delve into the tension between exploration and exploitation, particularly in high-stakes decision-making scenarios involving individuals. Examine how the greedy algorithm, which prioritizes immediate optimal decisions, can be analyzed in linear contextual bandit problems. Learn about the smoothed analysis approach, which demonstrates that small perturbations in adversarial context choices can lead to "no regret" performance. Investigate the implications for balancing exploration and exploitation in slightly perturbed environments. Cover topics such as classic algorithm design, online algorithms, online ML algorithms, single and multi-parameter models, regret analysis, diversity, and margins. Gain insights into the potential benefits and applications of greedy algorithms in various settings.