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Information-Theoretic Methods for Trustworthy Machine Learning
Location
Calvin Lab Auditorium
Date
Monday, May 22
–
Thursday, May 25, 2023
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Workshop & Symposia
Information-Theoretic Methods for Trustworthy Machine Learning
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Contraction of Markov Kernels and Differential Privacy (PART I)
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The Saddle-Point Accountant for Differential Privacy
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Towards “One-Shot” Privacy Auditing and Estimation
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Improving Accuracy-Privacy Tradeoff via Model Reprogramming
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KEYNOTE: Variational Formulations and Distributed Convex Optimization Methods for Generative Modeling in High Dimensions
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Fair, Explainable, and Lawful Machine Learning for High-Stakes Applications
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Information Thresholds in Structure Estimation
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Decision making with information-theoretic constraints
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The Limits of Group Fairness and Predictive Multiplicity
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Privacy and Fairness in Collaborative AI
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