Google TechTalks Courses

Auto-bidding in Online Advertising: Campaign Management and Fairness

Explore auto-bidding strategies in digital advertising, focusing on conversion maximization, ROI constraints, and fairness in auction outcomes. Learn efficient algorithms for multi-channel optimization.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

Online Covering: Secretaries, Prophets and Universal Maps

Explore polynomial-time algorithms for online covering IPs, including random-order constraints and prophet versions, with applications to universal maps and competitive ratios.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

Tree Learning: Optimal Algorithms and Sample Complexity - Hierarchical Clustering and PAC Learning

Explore optimal algorithms and sample complexity for learning hierarchical tree representations from labeled data, covering PAC learning, online learning, and efficient tree classifier construction.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

A Fast Algorithm for Adaptive Private Mean Estimation in Machine Learning

Explore adaptive private mean estimation with a fast algorithm, focusing on optimal convergence rates, computational efficiency, and adaptability beyond sub-Gaussian distributions.

• YouTube
• 54 minutes
• Self-Paced
• Free Video

Scaling DeFi with Radix Protocol - Web3 Talks

Explore Radix protocol's approach to scaling DeFi for global finance with CEO Piers Ridyard. Gain insights on permissionless systems, development velocity, and innovative solutions to crypto challenges.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

Fast Neural Kernel Embeddings for General Activations

Explore fast sketching methods for approximating multi-layered NNGP and NTK matrices with various activation functions, overcoming limitations of prior works and achieving significant speedups.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Pathwise Conditioning and Non-Euclidean Gaussian Processes

Explore advanced Gaussian process techniques for efficient Bayesian optimization, including pathwise conditioning and models on graphs and manifolds, enhancing decision-making in complex settings.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Video

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

Explore Fully Homomorphic Encryption for privacy-preserving machine learning, enabling zero-trust interactions and secure model deployment on untrusted servers.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Example Memorization in Learning: Batch and Streaming - Differential Privacy for ML

Explore example memorization in machine learning, covering batch and streaming scenarios, space requirements, and implications for natural models and future research directions.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

The Saddle Point Accountant for Differential Privacy in Machine Learning

Explore differential privacy's saddle point accountant method, its composition, and applications in machine learning through expert insights.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Differentially Private Multi-party Data Release for Linear Regression

Explore differential privacy techniques for secure multi-party data sharing in linear regression, enhancing privacy protection in collaborative machine learning environments.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

Machine Unlearning and Privacy Implications - Differential Privacy for ML

Explore privacy implications of machine unlearning, including threat models, deletion inference, and experimental results in this Google TechTalk on differential privacy for ML.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

Mapping from Training Data to Predictions with Datamodels - Differential Privacy in Machine Learning

Explore datamodels in machine learning, from training data to predictions. Learn about model brittleness, data counterfactuals, and applications in ML pipelines.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Marginal-based Methods for Differentially Private Synthetic Data - Differential Privacy for ML Series

Explore marginal-based methods for generating differentially private synthetic data, covering mechanisms, selection algorithms, and empirical findings.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Secure Self-supervised Learning: Challenges and Solutions

Explore secure self-supervised learning techniques, focusing on backdoor attacks, data auditing, and membership inference for pre-trained encoders in machine learning.

• YouTube
• 40 minutes
• Self-Paced
• Free Video