Google TechTalks
Differential PrivacyMachine LearningData PrivacyLarge Language Models (LLMs)Large Language ModelsDeep LearningAI SafetyPrompt EngineeringMachine Learning SecurityData poisoningMembership Inference AttacksCopyright InfringementNatural Language ProcessingData SecurityFine-tuningPrivacy AuditingLLM securityFederated LearningEthics of AIAdversarial AttacksStochastic Gradient DescentMatrix FactorizationPrivacy AlgorithmsLower BoundsModel EvaluationImage GenerationModel MemorizationMachine learning vulnerabilitiesSynthetic Data GenerationMachine Learning PrivacyRetrieval Augmented Generation (RAG)AI SecurityLanguage ModelsContinual CountingGenerative AIStreaming AlgorithmsApproximation AlgorithmsData MemorizationPrivacyPrivacy-Preserving Data AnalysisInformation Theory
Differential PrivacyPrivacy AmplificationMachine Learning
Privacy Amplification for Correlated-Noise Mechanisms via b-Min-Sep Subsampling
This research introduces B-min-sep subsampling, a novel method that enhances privacy amplification for differentially private matrix factorization (DPMF) by leveraging correlated noise and enabling practical application in complex multi-attribution settings.
Explore Insights →
Machine LearningData PrivacyUnlearning Algorithms
Leveraging Per-Instance Privacy for Machine Unlearning
This research reveals a theoretical and empirical framework for understanding and quantifying the difficulty of machine unlearning for individual data points, showing that unlearning steps scale logarithmically with per-instance privacy loss.
Explore Insights →
Want more on stochastic gradient descent?
Explore deep-dive summaries and actionable takeaways from the best minds across different podcasts discussing this topic.
View All Stochastic Gradient Descent Episodes→Don't see the episode you're looking for?
We're constantly adding new episodes, but if you want to see a specific one from Google TechTalks summarized, let us know!
Submit an Episode