The universal portfolio algorithm is a portfolio selection algorithm from the field of machine learning and information theory. The algorithm learns adaptively from historical data and maximizes the log-optimal growth rate in the long run. It was introduced by the late Stanford University information theorist Thomas M. Cover. The algorithm rebalances the portfolio at the beginning of each trading period. At the beginning of the first trading period it starts with a naive diversification. In the following trading periods the portfolio composition depends on the historical total return of all possible constant-rebalanced portfolios. (Wikipedia).
Nathanael Fijalkow: Understanding and extending the quasipolynomial time algorithms for parity games
This talk is about the model of two-player (deterministic) parity games, their extensions mean payoff games, and related game models. The dust has settled since the 2017 breakthrough—a quasipolynomial time algorithm for solving parity games. A lot of work has gone since then into understan
From playlist Workshop: Tropical geometry and the geometry of linear programming
What is the complement of a set? Sets in mathematics are very cool, and one of my favorite thins in set theory is the complement and the universal set. In this video we will define complement in set theory, and in order to do so you will also need to know the meaning of universal set. I go
From playlist Set Theory
Universal Set Example Problems | Set Builder Notation, Absolute Complement, Roster Notation
Set-builder notation with universal sets, absolute complements, the roster method, and more are all covered in today’s set theory math lesson! We go over three universal set example problems in this video. The first problem revolves around converting a set in set-builder notation to a se
From playlist Set Theory
Cedric Koh: Beyond value iteration for parity games: strategy iteration with universal trees
Parity games have witnessed several new quasi-polynomial algorithms since the breakthrough result of Calude et al. (2017). The central combinatorial object underlying these approaches is a universal tree, as identified by Czerwi´nski et al. (2019). By providing a quasi-polynomial lower bou
From playlist Workshop: Tropical geometry and the geometry of linear programming
Maths for Programmers: Sets (The Universe & Complements)
We're busy people who learn to code, then practice by building projects for nonprofits. Learn Full-stack JavaScript, build a portfolio, and get great references with our open source community. Join our community at https://freecodecamp.com Follow us on twitter: https://twitter.com/freecod
From playlist Maths for Programmers
Compute E Solution - 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 New Balancing Method for Solving Parametric Max Flow
March 14, 2007 lecture by Bin Zhang for the Stanford University Computer Systems Colloquium (EE 380). A new, simple and fast algorithm finds a sequence of nested minimum cuts of a bipartite parametric flow network. Instead of working with the original parametric flow-network, the new meth
From playlist Course | Computer Systems Laboratory Colloquium (2006-2007)
Portfolio Optimization API - Algorithmic Trading with Python and Quantopian p. 12
Once we've got combined alphas that we're happy with, we need to build a trading strategy. A major part of a trading strategy is figuring out how to best build your portfolio from the alphas, paying attention to various constraints like leverage, sector bias...etc. In this example, we use
From playlist Python Programming for Finance
Jana Cslovjecsek: Efficient algorithms for multistage stochastic integer programming using proximity
We consider the problem of solving integer programs of the form min {c^T x : Ax = b; x geq 0}, where A is a multistage stochastic matrix. We give an algorithm that solves this problem in fixed-parameter time f(d; ||A||_infty) n log^O(2d) n, where f is a computable function, d is the treed
From playlist Workshop: Parametrized complexity and discrete optimization
Dependence Uncertainty and Risk - Prof. Paul Embrechts
Abstract I will frame this talk in the context of what I refer to as the First and Second Fundamental Theorem of Quantitative Risk Management (1&2-FTQRM). An alternative subtitle for 1-FTQRM would be "Mathematical Utopia", for 2-FTQRM it would be "Wall Street Reality". I will mainly conce
From playlist Uncertainty and Risk
Is Finance Ready For Machine Learning? (Vivek Viswanathan) - KNN Ep. 54
Vivek Viswanathan is the portfolio manager of the Rayliant Quantamental China Equity ETF and is the Global Head of Research and Portfolio Management at Rayliant Global Advisors. He has a Ph.D. in Finance from UCI, a Master’s in Financial Engineering from UCLA, and a Bachelor’s in Economics
From playlist Ken's Nearest Neighbors Podcast
Bistra Dilkina: "Decision-focused learning: integrating downstream combinatorics in ML"
Deep Learning and Combinatorial Optimization 2021 "Decision-focused learning: integrating downstream combinatorics in ML" Bistra Dilkina - University of Southern California (USC) Abstract: Closely integrating ML and discrete optimization provides key advantages in improving our ability t
From playlist Deep Learning and Combinatorial Optimization 2021
Twenty third SIAM Activity Group on FME Virtual Talk Series
Date: Thursday, December 2, 2021, 1PM-2PM ET Speaker 1: Renyuan Xu, University of Southern California Speaker 2: Philippe Casgrain, ETH Zurich and Princeton University Moderator: Ronnie Sircar, Princeton Universit Join us for a series of online talks on topics related to mathematical fina
From playlist SIAM Activity Group on FME Virtual Talk Series
Leda Braga: Data science and its role in investment strategy
The CEO of Systematica Investments discusses how her company employs technology to achieve returns. As the first financial services industry speaker at the Women in Data Science (WiDS) conference, keynote presenter Leda Braga, CEO of Systematica Investments, introduced attendees to the ro
From playlist Women in Data Science Conference (WiDS) 2018
Strategizing - Algorithmic Trading with Python and Quantopian p. 9
In this tutorial, we cover how to begin realistically building a trading strategy. In the previous tutorials, we showed how we might go about finding a single alpha factor to trade stocks on, and then trading off of it, but this is almost certainly never going to be enough, so, here, we co
From playlist Python Programming for Finance
Udacity Alumni Network Presents: School of AI Open House
Enroll now - our next AI in Programming ND class starts August 7th: http://bit.ly/2GYd8Db ----- In this session you will: - Get an overview of the field of AI and how it’s applied in industry - Discover learning paths for mastering AI at Udacity and determine which is best for you - Ha
From playlist ML Talks by Luis Serrano
Can You Validate These Emails?
Email Validation is a procedure that verifies if an email address is deliverable and valid. Can you validate these emails?
From playlist Fun
Ivan Guo: Stochastic Optimal Transport in Financial Mathematics
Abstract: In recent years, the field of optimal transport has attracted the attention of many high-profile mathematicians with a wide range of applications. In this talk we will discuss some of its recent applications in financial mathematics, particularly on the problems of model calibra
From playlist SMRI Seminars