Information Thresholds in Structure Estimation

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=LQT_idestBA


Category:
Guide
Duration: 31:21
289 views
6

Anand Sarwate (Rutgers University)
https://simons.berkeley.edu/talks/anand-sarwate-rutgers-university-2023-05-24
Information-Theoretic Methods for Trustworthy Machine Learning



Other Videos By Simons Institute for the Theory of Computing


2023-05-26Overparameterized learning: an asymptotic toy model that shows interesting phenomena
2023-05-26Learning classification metrics from preference feedback
2023-05-26Differentially Quantized Gradient Methods
2023-05-26Data Valuation: A Perturbation-Aware Approach
2023-05-26Memorization in Machine Learning
2023-05-26Distribution estimation with user-level privacy and communication constraints
2023-05-25KEYNOTE: Information Constrained Optimal Transport
2023-05-25Privacy and Fairness in Collaborative AI
2023-05-25The Limits of Group Fairness and Predictive Multiplicity
2023-05-25Decision making with information-theoretic constraints
2023-05-25Information Thresholds in Structure Estimation
2023-05-25Fair, Explainable, and Lawful Machine Learning for High-Stakes Applications
2023-05-24KEYNOTE: Variational Formulations and Distributed Convex Optimization Methods for...
2023-05-24Improving Accuracy-Privacy Tradeoff via Model Reprogramming
2023-05-24Towards “One-Shot” Privacy Auditing and Estimation
2023-05-24The Saddle-Point Accountant for Differential Privacy
2023-05-24Contraction of Markov Kernels and Differential Privacy (PART II)
2023-05-24Contraction of Markov kernels and differential privacy (PART I)
2023-05-23KEYNOTE: Differential Privacy & Variants
2023-05-23Information-theoretic Foundations of Generative Adversarial Models: ...
2023-05-23Generalization bounds for Neural Network Based Decoders


Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Information-Theoretic Methods for Trustworthy Machine Learning
Anand Sarwate