Simons Institute Courses

Auto-Bidding, Auctions, and Ad Allocation

Explore automated bidding in online advertising, its impact on auction design, and challenges in ad allocation. Learn about recent research at the intersection of auto-bidding and auction theory.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Matching and Disclosure

Explore economic theories of matching and disclosure in market design, focusing on online platforms and their impact on market efficiency and participant strategies.

• YouTube
• 32 minutes
• On-Demand
• Free Video

Simple Proofs of Important Results in Market Design

Explore key algorithms and theorems in matching theory and market design, focusing on deferred acceptance and top trading cycles, with simple proofs of fundamental concepts.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Streaming - Synchronous, Recursion, and Incremental Computation

Explore synchronous streaming operators for elegant support of recursion and incremental computation in databases. Learn about algebraic equivalences and their analogy to discrete signal processing.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Sketching for Proving Generalization of Support Vector Machines

Explore how sketching and Johnson-Lindenstrauss transform improve generalization bounds for Support Vector Machines, enhancing binary classification and hyperplane separation techniques.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Polylogues

Explore the intersection of math and theoretical computer science with Nikhil Srivastava and Venkatesan Guruswami as they discuss their roles at the Simons Institute and Srivastava's research.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Sketching Techniques for Kernel Density Estimation or for Optimal Transport Computations

Explore advanced sketching techniques for kernel density estimation and optimal transport computations, enhancing algorithmic efficiency in data analysis and machine learning.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Memory-Regret Tradeoff for Online Learning

Explores algorithms for online learning with limited memory, improving memory-regret tradeoffs against oblivious and adaptive adversaries. Discusses new techniques and lower bounds in the experts problem.

• YouTube
• 58 minutes
• On-Demand
• Free Video

Memory-Regret Tradeoff for Online Learning

Explore the balance between memory usage and regret minimization in online learning algorithms, examining theoretical foundations and practical implications.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Sketching Techniques for Kernel Density Estimation

Explore advanced sketching techniques for efficient kernel density estimation, focusing on algorithmic design and computational improvements in high-dimensional data analysis.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Fast Attention Requires Bounded Entries

Explore the relationship between fast attention mechanisms and bounded entries in machine learning, with insights from Josh Alman's research on sketching and algorithm design.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Randomized Embeddings and Neural Networks

Exploring connections between random embeddings and neural networks through convex analysis, focusing on ReLU networks, randomized algorithms, and their implications for training efficiency.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Capacity Analysis of Vector Symbolic Architectures

Explores representation capacities of vector symbolic architectures, analyzing their ability to perform symbolic tasks and comparing them to a novel Hopfield network variant.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Improved Frequency Estimation Algorithms With and Without Predictions

Explore advanced frequency estimation algorithms for data streams, including novel approaches outperforming classical methods and further improvements using heavy-hitter predictions.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Testing Positive Semidefiniteness and Eigenvalue Approximation

Explore efficient algorithms for testing positive semidefiniteness and approximating eigenvalues in large matrices, with insights from David Woodruff of Carnegie Mellon University.

• YouTube
• 35 minutes
• On-Demand
• Free Video