Bandits - Kevin Jamieson - University of Washington
Explore bandit algorithms and their applications in machine learning, from drug development to content recommendation, with a focus on regret minimization and stochastic models.
Theory Seminar - Spectral Independence in High-Dimensional Expanders, Kuikui Liu
Explore spectral independence in high-dimensional expanders and its application to the hardcore model, including rapid mixing of Glauber dynamics for generating random independent sets.
PageRank - A Trillion Dollar Algorithm
Explore the mathematical foundations and complexities of PageRank, the revolutionary search algorithm. Learn about Markov chains, stationary distributions, and their practical applications in web search technology.
Benefits of Saying I Don't Know When Analyzing and Modeling the Climate System With ML - Elizabeth Barnes
Explore the benefits of acknowledging uncertainty in climate system analysis using machine learning. Learn about state-dependent predictability and controlled abstention networks for improved modeling accuracy.
Interpretable and Generalizable Machine Learning for Fluid Dynamics - Steven Brunton
Explore interpretable and generalizable machine learning techniques for fluid dynamics, focusing on sparse identification of nonlinear dynamics and incorporating physical constraints for improved accuracy.
Introduction to Optimization
Learn optimization techniques for machine learning, including likelihood estimation, gradient descent, and regularization, with practical examples and applications.
Quantum "Supremacy" - Lecture 25 of Quantum Computation at CMU
Explore quantum supremacy, its challenges, and implications in computational theory. Delve into error correction, threshold theorems, and the potential of quantum systems.
Basics of Quantum Computing - Lecture 10 of Quantum Computation at CMU
Explore quantum computing fundamentals, including efficiency, probabilities, circuits, gates, and reversible computation. Gain insights into key concepts and important questions in this field.
Optimal Sampling in Weighted Least-Squares Methods - Application to High-Dimensional Approximation
Explore optimal sampling in weighted least-squares for high-dimensional approximation, focusing on mathematical foundations and modern approaches to overcome the curse of dimensionality.
Modeling Multisector Dynamics to Inform Adaptive Pathways
Explore multisector dynamics research for understanding complex system interactions and informing adaptive strategies across technological, societal, natural, and managed sectors.
Emergent Phenomena in Random Structures and Algorithms
Explore emergent phenomena in random structures and algorithms, focusing on mathematical insights and applications in complex systems and computational methods.
Day-to-Day Testing of Linux 'Next' Kernel Branch
Explore daily testing of Linux 'next' kernel branch, including system setup, regression identification, reporting, and automated bisection. Learn practical tips for finding and fixing kernel issues.
Dragon-Kings, Black-Swans and Prediction - Lorenz Lecture
Explore extreme events, their predictability, and impact on complex systems through statistical physics and nonlinear dynamics in this insightful lecture by D. Sornette.
Interacting Particle Systems for Expectation Maximization in Latent Variable Models
Explore a novel interacting particle system for EM optimization in latent variable models, featuring Langevin diffusion analysis and non-asymptotic concentration bounds for maximum likelihood estimation.
Approximation Algorithms for Stochastic Minimum Norm Combinatorial Optimization
Explore advanced techniques for solving stochastic minimum norm combinatorial optimization problems, focusing on approximation algorithms and their applications in complex decision-making scenarios.
Pandora's Box with Correlations - Learning and Approximation
Explore learning and approximation techniques for correlated Pandora's Box problems, focusing on algorithmic approaches and theoretical insights.
Learning Algorithms and Techniques - Session 1C
Explore cutting-edge research in machine learning and computational theory at FOCS 2020's Session 1C, featuring presentations on innovative algorithms and theoretical advancements.
Hitting the High-Dimensional Notes - An ODE for SGD Learning Dynamics in High-Dimensions
Explore stochastic optimization algorithms in high dimensions, analyzing dynamics and performance of SGD and momentum methods using a framework inspired by random matrix theory.
Using Equivalence Classes to Accelerate Solving the Discrete Logarithm Problem in a Short Interval
Explore techniques to accelerate solving the discrete logarithm problem using equivalence classes, including fast inversion, distinguishing points, and random walks.
Privacy Preserving Aggregate Statistics via Boolean Shares - Part 3
Privacy-preserving aggregate statistics using Boolean shares, enhancing the Prio system for improved data collection and analysis while maintaining user privacy.
Quantum Speedups of Continuous Sampling and Optimization Problems - IPAM at UCLA
Explore quantum algorithms for high-dimensional sampling and optimization, including log-concave distributions and approximately convex problems, with applications in statistics, machine learning, and physics.