YoVDO

Improving Accuracy-Privacy Tradeoff via Model Reprogramming

Offered By: Simons Institute via YouTube

Tags

Machine Learning Courses Data Security Courses Trustworthy Machine Learning Courses

Course Description

Overview

Explore a 36-minute lecture by Pin-Yu Chen from IBM Research on enhancing the balance between accuracy and privacy in machine learning through model reprogramming techniques. Delve into information-theoretic methods for developing trustworthy AI systems and gain insights into innovative approaches for optimizing the accuracy-privacy tradeoff in modern machine learning applications.

Syllabus

Improving Accuracy-Privacy Tradeoff via Model Reprogramming


Taught by

Simons Institute

Related Courses

Information-Theoretic Foundations of Generative Adversarial Models
Simons Institute via YouTube Generalization Bounds for Neural Network Based Decoders
Simons Institute via YouTube Optimal Neural Network Compressors and the Manifold Hypothesis
Simons Institute via YouTube Fundamental Trade-Offs in FL-FA - Sparsity - DP - Communication Constraints
Simons Institute via YouTube Contraction of Markov Kernels and Differential Privacy - Part I
Simons Institute via YouTube