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Explore multi-objective optimization in machine learning through this 41-minute seminar that challenges the traditional focus on prediction accuracy alone. Learn how to systematically discover and navigate trade-offs between competing objectives such as cost, regulatory compliance, explainability, privacy, fairness, and energy efficiency in ML model deployment. Discover how to frame these complex decisions as multi-objective optimization problems and apply Bayesian optimization techniques to solve them effectively. Examine concrete case studies demonstrating practical applications across privacy preservation, fairness considerations, and energy efficiency optimization. Gain an intuitive understanding of how to identify and balance multiple competing objectives in your own ML projects, moving beyond ad-hoc constraints and arbitrary thresholds toward principled decision-making frameworks that can improve existing practices and reveal new insights for real-world machine learning deployment.
