Simons Institute Courses

Scalable and Reliable Inference for Probabilistic Modeling

Explore scalable and reliable inference techniques for probabilistic modeling, focusing on Metropolis-Hastings, Gibbs sampling, and stochastic gradient MCMC methods for efficient data analysis and deep learning applications.

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

Wasserstein GANs Work Because They Fail - to Approximate the Wasserstein Distance

Explore the counterintuitive success of Wasserstein GANs despite their failure to accurately approximate the Wasserstein distance, offering insights into their effectiveness in generative modeling.

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

Panel on Quantum Machine Learning and Barren Plateaus

Expert panel explores quantum machine learning challenges, focusing on barren plateaus and their implications for algorithm design and practical quantum computing applications.

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

Tensor Graphical Models for Complex and High Dimensional Data

Explore advanced statistical methods for analyzing complex, high-dimensional data using tensor graphical models, with applications in algorithmic inference and combinatorial structures.

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

An Efficient Quantum Algorithm for Lattice Problems Achieving Subexponential Approximation Factor

Explore a groundbreaking quantum algorithm for lattice problems, offering subexponential approximation and exponential speedup over classical methods in polynomial time.

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

Hidden Symmetries of Computational Problems

Explore hidden symmetries in computational problems, uncovering geometric methods for optimization and sampling with insights from experts in mathematics and quantum computing.

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

Computational Optimal Transport

Explore geometric methods in optimization and sampling, focusing on computational optimal transport techniques and their applications in machine learning and data analysis.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Video

An Introduction to Hamiltonian Monte Carlo Method for Sampling

Explore the Hamiltonian Monte Carlo method for efficient sampling in high-dimensional spaces, its theoretical foundations, and practical applications in optimization and machine learning.

• YouTube
• 1 hour 10 minutes
• Self-Paced
• Free Video

Sampling Using Diffusion Processes, From Langevin to Schrödinger

Explore diffusion processes in sampling, from Langevin dynamics to Schrödinger bridges, with insights on geometric methods in optimization and their applications.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Geodesic Convexity and Optimization

Explore geodesic convexity and its applications in optimization, delving into geometric methods for solving complex problems in high-dimensional spaces.

• YouTube
• 1 hour 12 minutes
• Self-Paced
• Free Video

Optimization on Manifolds

Learn techniques for optimizing functions on smooth manifolds, exploring applications in machine learning and data science with practical examples and toolbox demonstrations.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Optimization Crash Course

Explore advanced optimization techniques and geometric methods for efficient problem-solving in this in-depth lecture by MIT's Ashia Wilson.

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

Optimal Iterative Algorithms for Problems With Random Data

Explore advanced iterative algorithms for solving problems with random data, focusing on computational complexity in statistical inference.

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

Optimal Iterative Algorithms for Problems With Random Data

Explore optimal iterative algorithms for statistical problems with random data, focusing on computational complexity and efficient solutions in statistical inference.

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

Summer Cluster in Quantum Computation - Polylogues

Explore key themes in quantum computation with experts discussing complexity theory, protocols, entanglement, algorithms, and quantum chemistry in this insightful panel discussion.

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