Models of computation | Statistical randomness
Stochastic computing is a collection of techniques that represent continuous values by streams of random bits. Complex computations can then be computed by simple bit-wise operations on the streams. Stochastic computing is distinct from the study of randomized algorithms. (Wikipedia).
Basic stochastic simulation b: Stochastic simulation algorithm
(C) 2012-2013 David Liao (lookatphysics.com) CC-BY-SA Specify system Determine duration until next event Exponentially distributed waiting times Determine what kind of reaction next event will be For more information, please search the internet for "stochastic simulation algorithm" or "kin
From playlist Probability, statistics, and stochastic processes
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
An introduction to multilevel Monte Carlo methods – Michael Giles – ICM2018
Numerical Analysis and Scientific Computing Invited Lecture 15.7 An introduction to multilevel Monte Carlo methods Michael Giles Abstract: In recent years there has been very substantial growth in stochastic modelling in many application areas, and this has led to much greater use of Mon
From playlist Numerical Analysis and Scientific Computing
Introduction to the paper https://arxiv.org/abs/2002.06707
From playlist Research
Iterative stochastic numerical methods for statistical sampling: Professor Ben Leimkuhler
I study the design, analysis and implementation of algorithms for time-dependent phenomena and modelling for problems in engineering and the sciences. My previous works have helped to establish the foundations of molecular simulation, providing efficient deterministic and stochastic numeri
From playlist Data science classes
"Data-Driven Optimization in Pricing and Revenue Management" by Arnoud den Boer - Lecture 1
In this course we will study data-driven decision problems: optimization problems for which the relation between decision and outcome is unknown upfront, and thus has to be learned on-the-fly from accumulating data. This type of problems has an intrinsic tension between statistical goals a
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
Hybrid sparse stochastic processes and the resolution of (...) - Unser - Workshop 2 - CEB T1 2019
Michael Unser (EPFL) / 12.03.2019 Hybrid sparse stochastic processes and the resolution of linear inverse problems. Sparse stochastic processes are continuous-domain processes that are specified as solutions of linear stochastic differential equations driven by white Lévy noise. These p
From playlist 2019 - T1 - The Mathematics of Imaging
"Data-Driven Optimization in Pricing and Revenue Management" by Arnoud den Boer - Lecture 3
In this course we will study data-driven decision problems: optimization problems for which the relation between decision and outcome is unknown upfront, and thus has to be learned on-the-fly from accumulating data. This type of problems has an intrinsic tension between statistical goals a
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
"Diffusion Approximation and Sequential Experimentation" by Victor Araman
We consider a Bayesian sequential experimentation problem. We identify environments in which the average number of experiments that is conducted per unit of time is large and the informativeness of each individual experiment is low. Under such regimes, we derive a diffusion approximation f
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
Jorge Nocedal: "Tutorial on Optimization Methods for Machine Learning, Pt. 3"
Graduate Summer School 2012: Deep Learning, Feature Learning "Tutorial on Optimization Methods for Machine Learning, Pt. 3" Jorge Nocedal, Northwestern University Institute for Pure and Applied Mathematics, UCLA July 18, 2012 For more information: https://www.ipam.ucla.edu/programs/summ
From playlist GSS2012: Deep Learning, Feature Learning
Introductory lectures on first-order convex optimization (Lecture 2) by Praneeth Netrapalli
DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n
From playlist Statistical Physics of Machine Learning 2020
25. Stochastic Gradient Descent
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Suvrit Sra View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k Professor Suvrit Sra g
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Deep Learning Lecture 4.3 - Stochastic Gradient Descent
Deep Learning Lecture: Optimization Methods - Stochastic Gradient Descent (SGD) - SGD with Momentum
From playlist Deep Learning Lecture
Stochastic Gradient Descent and Machine Learning (Lecture 3) by Praneeth Netrapalli
PROGRAM: BANGALORE SCHOOL ON STATISTICAL PHYSICS - XIII (HYBRID) ORGANIZERS: Abhishek Dhar (ICTS-TIFR, India) and Sanjib Sabhapandit (RRI, India) DATE & TIME: 11 July 2022 to 22 July 2022 VENUE: Madhava Lecture Hall and Online This school is the thirteenth in the series. The schoo
From playlist Bangalore School on Statistical Physics - XIII - 2022 (Live Streamed)
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
Stochastic Inflation and Primordial Black Holes - V. Vennin - Workshop 1 - CEB T3 2018
Vincent Vennin (APC Paris) / 17.09.2018 Stochastic Inflation and Primordial Black Holes ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : https://twitter.
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
From playlist Contributed talks One World Symposium 2020
Dr Lukasz Szpruch, University of Edinburgh
Bio I am a Lecturer at the School of Mathematics, University of Edinburgh. Before moving to Scotland I was a Nomura Junior Research Fellow at the Institute of Mathematics, University of Oxford, and a member of Oxford-Man Institute for Quantitative Finance. I hold a Ph.D. in mathematics fr
From playlist Short Talks
AQC 2016 - Adiabatic Quantum Computer vs. Diffusion Monte Carlo
A Google TechTalk, June 29, 2016, presented by Stephen Jordan (NIST) ABSTRACT: While adiabatic quantum computation using general Hamiltonians has been proven to be universal for quantum computation, the vast majority of research so far, both experimental and theoretical, focuses on stoquas
From playlist Adiabatic Quantum Computing Conference 2016