In numerical optimization, meta-optimization is the use of one optimization method to tune another optimization method. Meta-optimization is reported to have been used as early as in the late 1970s by Mercer and Sampson for finding optimal parameter settings of a genetic algorithm. Meta-optimization and related concepts are also known in the literature as meta-evolution, super-optimization, automated parameter calibration, hyper-heuristics, etc. (Wikipedia).
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
Intro Into Multi Objective Optimization
Multi-objective optimization (also known as multi-objective programming, vector optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective func
From playlist Software Development
What Is Mathematical Optimization?
A gentle and visual introduction to the topic of Convex Optimization. (1/3) This video is the first of a series of three. The plan is as follows: Part 1: What is (Mathematical) Optimization? (https://youtu.be/AM6BY4btj-M) Part 2: Convexity and the Principle of (Lagrangian) Duality (
From playlist Convex Optimization
13_2 Optimization with Constraints
Here we use optimization with constraints put on a function whose minima or maxima we are seeking. This has practical value as can be seen by the examples used.
From playlist Advanced Calculus / Multivariable Calculus
Calculus: Optimization Problems
In this video, I discuss optimization problems. I give an outline for how to approach these kinds of problems and worth through a couple of examples.
From playlist Calculus
Calculus: We present a procedure for solving word problems on optimization using derivatives. Examples include the fence problem and the minimum distance from a point to a line problem.
From playlist Calculus Pt 1: Limits and Derivatives
13_1 An Introduction to Optimization in Multivariable Functions
Optimization in multivariable functions: the calculation of critical points and identifying them as local or global extrema (minima or maxima).
From playlist Advanced Calculus / Multivariable Calculus
Optimization Problems in Calculus
What good is calculus anyway, what does it have to do with the real world?! Well, a lot, actually. Optimization is a perfect example! If you want to figure out how to maximize your profits or minimize your costs, or if you want to maximize an area or minimize a distance, you are finding th
From playlist Calculus
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
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 3 - Optimization-Based Meta-Learning
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/
From playlist Stanford CS330: Deep Multi-Task and Meta Learning
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 4 - Non-Parametric Meta-Learners
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/
From playlist Stanford CS330: Deep Multi-Task and Meta Learning
Stanford CS330:Multi-task and Meta Learning | 2020 | Lecture 11:Meta RL: Adaptable Models & Policies
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.
From playlist Stanford CS330: Deep Multi-task and Meta Learning | Autumn 2020
Stanford CS330 Deep Multi-Task & Meta Learning - Optimization-Based Meta-Learning l 2022 I Lecture 5
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Chelsea Finn Computer
From playlist Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
Stanford CS330: Deep Multi-task and Meta Learning | 2020 | Lecture 4 - Optimization Meta-Learning
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.
From playlist Stanford CS330: Deep Multi-task and Meta Learning | Autumn 2020
Stanford CS330 I Advanced Meta-Learning 2: Large-Scale Meta-Optimization l 2022 I Lecture 10
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Chelsea Finn Computer
From playlist Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 11
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To follow along with the course, visit: http://cs330.stanford.edu/fall2021/index.html To view all online courses and programs offered by Stanford, visit: http:/
From playlist Stanford CS330: Deep Multi-Task & Meta Learning I Autumn 2021I Professor Chelsea Finn
iMAML: Meta-Learning with Implicit Gradients (Paper Explained)
Gradient-based Meta-Learning requires full backpropagation through the inner optimization procedure, which is a computational nightmare. This paper is able to circumvent this and implicitly compute meta-gradients by the clever introduction of a quadratic regularizer. OUTLINE: 0:00 - Intro
From playlist Papers Explained
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 2 - Multi-Task & Meta-Learning Basics
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/ 0:00 Introduction 0:12 Logistics 1:42 Plan for Today 2:57 Some notation 7:00 Ex
From playlist Stanford CS330: Deep Multi-Task and Meta Learning
[Calculus] Optimization 1 || Lecture 34
Visit my website: http://bit.ly/1zBPlvm Subscribe on YouTube: http://bit.ly/1vWiRxW Hello, welcome to TheTrevTutor. I'm here to help you learn your college courses in an easy, efficient manner. If you like what you see, feel free to subscribe and follow me for updates. If you have any que
From playlist Calculus 1
Meta-Learning: Why It’s Hard and What We Can Do - Ke Li
Seminar on Theoretical Machine Learning Topic: Meta-Learning: Why It’s Hard and What We Can Do Speaker: Ke Li Affiliation: Member, School of Mathematics Date: April 9, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics