Simons Institute Courses

Federated Heavy Hitters Discovery with Differential Privacy

Explore federated learning, differential privacy, and heavy hitters discovery in this talk by Peter Kairouz. Learn about multiparty computation, open research, and practical applications in data analysis.

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

Privacy Amplification by Sampling and Renyi Differential Privacy

Explore privacy amplification, Renyi differential privacy, and sampling techniques in data analysis. Learn about SGD, use cases, and technical results for enhancing privacy in various applications.

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

Generative Adversarial Models for Privacy and Fairness

Explore generative adversarial models for enhancing privacy and fairness in machine learning, covering differential privacy, GANs, and real-world applications in data publishing and encoding.

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

The Space of Lorentzian Polynomials

Explore Lorentzian polynomials, projective varieties, and discrete convexity with June Huh. Delve into quadratic forms, M-convexity, and algebraic intuition in this advanced mathematical lecture.

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

Foundations and Applications of Data Privacy - A Perspective

Explore data privacy foundations, challenges, and applications, focusing on differential privacy concepts and their real-world implementations in tech and government sectors.

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

Computing Partition Functions, Part I

Explore computational techniques for partition functions in statistical physics, focusing on spin systems, phase transitions, and algorithmic approaches like Markov Chain Monte Carlo.

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

Real-Rooted Polynomials

Explore real-rooted polynomials, their properties, and connections to combinatorics in this comprehensive lecture covering fundamental theorems, root interlacing, and applications to random spanning trees.

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

Berkeley in the 80s - Episode 1- Shafi Goldwasser

Insightful interview with Turing Laureate Shafi Goldwasser, exploring her groundbreaking research in theoretical computer science, interactive proofs, and zero-knowledge protocols at UC Berkeley in the 1980s.

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

Invariant Theory for Computer Scientists

Explore algebraic methods in computer science, focusing on invariant theory, graph isomorphism, and group actions. Learn about algorithms, moment polytopes, and open problems in the field.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

The Limits of Proof

Explore proof systems, from classical logic to modern computational approaches, examining their strengths, limitations, and applications in mathematics and computer science.

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

Sketching Big Data

Explore big data sketching techniques with Harvard's Jelani Nelson, covering approximate counting, frequent items, and graph streaming in this insightful lecture.

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

K-Means and K-Medians Under Dimension Reduction

Explore k-means and k-medians clustering algorithms under dimension reduction, examining distortion graphs, cost analysis, and combinatorial lemmas for robust high-dimensional statistics.

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

Can Non-Convex Optimization Be Robust?

Explore non-convex optimization's robustness, examining locally optimizable functions, perturbed objectives, and matrix completion. Insights on empirical vs. population risk and semi-random adversaries.

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

Robust Estimation and Generative Adversarial Nets

Explore robust estimation techniques and their applications in high-dimensional statistics, including Huber's model, multivariate location depth, and f-Learning, with insights on computational challenges and theoretical foundations.

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

Invariance, Causality and Novel Robustness

Explore invariance, causality, and robustness in high-dimensional statistics. Learn about modern applications, heterogeneous data, and novel approaches to prediction problems using causal inference techniques.

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