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Explore data valuation through game theory in this 38-minute seminar from GERAD Research Center examining how individual data points contribute to machine learning performance. Learn about the challenges organizations face when using data Shapley values for fair valuation, particularly how zero-value assignments can discourage data sharing consent. Discover an innovative Information Disclosure Game framework between Data Owners and Data Consumers that addresses these issues through incremental information revelation with Laplacian noise under differential privacy constraints. Examine k-nearest neighbors algorithms and Data Consumer strategies using data Shapley values and multi-armed bandit exploration techniques. Analyze experimental results from Yelp review tasks that demonstrate the real costs data valuation imposes on selection processes, presented by Patrick Mesana from HEC Montréal in collaboration with Gilles Caporossi and Sébastien Gambs.
