Nikhil Bansal: On a generalization of iterated and randomized rounding
The lecture was held within the framework of the follow-up workshop to the Hausdorff Trimester Program: Combinatorial Optimization. We describe a new rounding procedure that optimally combines the benefits of both iterated rounding and randomized rounding. A nice feature of this procedure
From playlist Follow-Up-Workshop "Combinatorial Optimization"
Randomness Extraction: A Survey - David Zuckerman
David Zuckerman University of Texas at Austin; Institute for Advanced Study February 7, 2012 A randomness extractor is an efficient algorithm which extracts high-quality randomness from a low-quality random source. Randomness extractors have important applications in a wide variety of area
From playlist Mathematics
Random Oracle - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Viswanath Nagarajan: Approximation Friendly Discrepancy Rounding
We consider the general problem of rounding a fractional vector to an integral vector while (approximately) satisfying a number of linear constraints. Randomized rounding and discrepancy-based rounding are two of the strongest rounding methods known. However these algorithms are very diffe
From playlist HIM Lectures: Trimester Program "Combinatorial Optimization"
Randomness - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
A very basic overview of optimization, why it's important, the role of modeling, and the basic anatomy of an optimization project.
From playlist Optimization
Randomness Quiz - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Comparing Bayesian optimization with traditional sampling
Welcome to video #2 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video Sterling introduces Bayesian Optimization as an alternative method for sa
From playlist Optimization tutorial
Questions And Answers - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Fellow Short Talks: Dr Peter Richtarik, Edinburgh University
Peter Richtarik is a Reader in the School of Mathematics at the University of Edinburgh, and is the Head of a Big Data Optimization Lab. He received his PhD from Cornell University in 2007, and currently holds an EPSRC Early Career Fellowship in Mathematical Sciences. RESEARCH My main re
From playlist Short Talks
8 2 Randomized Selection Analysis 21 min
From playlist Algorithms 1
Optimal shape and location of sensors or actuators in PDE models – Emmanuel Trélat – ICM2018
Control Theory and Optimization Invited Lecture 16.1 Optimal shape and location of sensors or actuators in PDE models Emmanuel Trélat Abstract: We report on a series of works done in collaboration with Y. Privat and E. Zuazua, concerning the problem of optimizing the shape and location o
From playlist Control Theory and Optimization
Stochastic Gradient Descent: where optimization meets machine learning- Rachel Ward
2022 Program for Women and Mathematics: The Mathematics of Machine Learning Topic: Stochastic Gradient Descent: where optimization meets machine learning Speaker: Rachel Ward Affiliation: University of Texas, Austin Date: May 26, 2022 Stochastic Gradient Descent (SGD) is the de facto op
From playlist Mathematics
MFEM Workshop 2022 | Stochastic Fractional PDEs: Random Field Generation & Topology Optimization
The LLNL-led MFEM (Modular Finite Element Methods) project provides high-order mathematical calculations for large-scale scientific simulations. The project’s second community workshop was held on October 25, 2022, with participants around the world. Learn more about MFEM at https://mfem.o
From playlist MFEM Community Workshop 2022
Applied Machine Learning 2019 - Lecture 13 - Parameter Selection and Automatic Machine Learning
Grid Search, Randomized Search Bayesian Optimization, SMBO Successive halving, hyperband auto-sklearn Freely borrowed materials from https://www.youtube.com/watch?v=0eBR8a4MQ30 which you should probably watch instead. Slides and more materials are on the class website: https://www.cs.col
From playlist Applied Machine Learning - Spring 2019
Andreas Mueller - Automated Machine Learning - AI With The Best Oct 2017
AI With The Best hosted 50+ speakers and hundreds of attendees from all over the world on a single platform on October 14-15, 2017. The platform held live talks, Insights/Questions pages, and bookings for 1-on-1s with speakers. Recent years have seen a widespread adoption of machine learn
From playlist talks
Piotr Indyk - Learning-Based Low-Rank Approximations - IPAM at UCLA
Recorded 29 November 2022. Piotr Indyk of the Massachusetts Institute of Technology presents "Learning-Based Low-Rank Approximations" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: I will give an overview of the recent line of work on learning-based algor
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
“Choice Modeling and Assortment Optimization” - Session I - Prof. Huseyin Topaloglu
This module overviews static and dynamic assortment optimization problems. We start with an introduction to discrete choice modeling and discuss estimation issues when fitting a choice model to observed sales histories. Following this introduction, we discuss static and dynamic assortment
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
What in the world is a linear program?
What is a linear program and why do we care? Today I’m going to introduce you to the exciting world of optimization, which is the mathematical field of maximizing or minimizing an objective function subject to constraints. The most fundamental topic in optimization is linear programming,
From playlist Summer of Math Exposition 2 videos
Traditional sampling techniques (grid vs random vs sobol vs latin hypercube)
Welcome to video #1 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video, Sterling introduces the concept of adaptive experimentation and covers t
From playlist Optimization tutorial