Convex Optimization
Learn Convex Optimization in under an hour with NIOS's concise and focused material. Prof. Joydeep Dutta.
The Value of Collaboration in Convex Machine Learning with Differential Privacy
Explores collaborative machine learning on distributed private data using differentially-private gradients, quantifying trade-offs between privacy and utility, and validating performance predictions with real-world financial datasets.
Theory Seminar - Differential Privacy in Theory and Practice, Abie Flaxman
Explore differential privacy in the US Census, its impact on data accuracy, and potential improvements. Insights from analyzing test runs and 1940 data application.
Computational Methods for Mean-Field Games
Explore computational methods for mean-field game systems, including finite-difference, convex optimization, Lagrangian, and machine-learning techniques for solving high-dimensional Hamilton-Jacobi PDEs.
Polar Deconvolution of Mixed Signals - IMAGINE Seminar Series
Explore signal demixing using polar deconvolution, a novel approach for separating mixed signals with high efficiency and stability, presented by Michael Friedlander from UBC.
Private Convex Optimization via Exponential Mechanism - Differential Privacy for Machine Learning
Explore private convex optimization using the exponential mechanism. Learn differential privacy, noisy SGD, and isoperimetric inequality for ML applications.
Convex Optimization I - Fundamentals and Applications
Explore fundamental concepts and applications of convex optimization with Microsoft researcher Sebastien Bubeck in this comprehensive lecture on mathematical programming techniques.
Do We Really Need All That Data? Learning and Control for Contact-rich Manipulation
Explore data efficiency in robotic manipulation, focusing on contact-driven aspects and implicit learning techniques for improved adaptability and real-time multi-contact MPC deployment.
A Potential Approach for One-Dimensional Price Formation Models
Explore a novel approach to solving first-order mean-field games in price formation using Poincaré's Lemma and potential functions, with applications to linear-quadratic models.
Strictly-Correlated Electron Functional and Multimarginal Optimal Transport
Explore convex optimization methods for multi-marginal transport problems in density functional theory, including relaxation techniques and rounding schemes for energy bounds.
Rigidity Theory for Gaussian Graphical Models - The Maximum Likelihood Threshold of a Graph
Explore rigidity theory's application to Gaussian graphical models, focusing on maximum likelihood thresholds and their implications for data analysis in genomics and other fields.
Convex Optimization II - Advanced Techniques and Applications
Explore advanced concepts in convex optimization with Microsoft Research expert Sebastien Bubeck, delving into cutting-edge techniques and applications in this field.
Computational Optimal Transport - A Mini Tutorial
Overview of novel approaches to compute optimal transport, blending recent techniques including convex optimization, automatic differentiation, and neural networks for large-scale approximations in high-dimensional regimes.
Optimisation - An Introduction: Professor Coralia Cartis, University of Oxford
Explore optimization techniques, from basic concepts to advanced methods, covering minimizers, derivatives, quadratic functions, and gradient methods for solving complex mathematical problems.
Convex Optimization V - Sebastien Bubeck - Microsoft Research
Explore advanced optimization techniques with Sebastien Bubeck, covering telescopic sums, continuous time analysis, and variance reduction in convex optimization.
Convex Optimization IV - Sebastien Bubeck, Microsoft Research
Explore advanced concepts in convex optimization, including gradient flow, comparison techniques, and optimal algorithms with Sebastien Bubeck from Microsoft Research.
Convex Optimization III - Sebastien Bubeck - Microsoft Research
Explore advanced concepts in convex optimization, including gradient oracles, minimax principles, and efficient algorithms for solving complex optimization problems.
An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games
Explore advanced cutting plane methods for convex optimization and games, covering improved algorithms, runtime bottlenecks, data structures, and practical applications in computational mathematics.
A Lower Bound for Parallel Submodular Minimization
Explore lower bounds for parallel submodular minimization, examining adaptive complexity models and exponentially faster algorithms for this optimization problem.
The Convex Geometry of Blind Deconvolution and Matrix Completion Revisited
Explores convex geometry in blind deconvolution and matrix completion, analyzing dimensional factors in noise bounds and proposing alternative error scaling approaches for low-rank matrix recovery problems.
Introductory Lectures on First-Order Convex Optimization - Lecture 1
Explore first-order convex optimization, covering gradient descent, complexity, smoothness, and Nesterov's accelerated algorithm. Gain insights into theoretical foundations and practical applications in machine learning.