Shmuel Onn: Sparse integer programming is FPT
We show that sparse integer programming, in variable dimension, with linear or separable convex objective, is fixed-parameter tractable. This is a culmination of a long line of research with many colleagues. We also discuss some of the many consequences of this result, which provides a new
From playlist Workshop: Tropical geometry and the geometry of linear programming
Tao Hou (5/13/20): Computing minimal persistent cycles: Polynomial and hard cases
Title: Computing minimal persistent cycles: Polynomial and hard cases Abstract: Persistent cycles, especially the minimal ones, are useful geometric features functioning as augmentations for the intervals in the purely topological persistence diagrams (also termed as barcodes). In our ear
From playlist AATRN 2020
Convergent sequences are bounded
Convergent Sequences are Bounded In this video, I show that if a sequence is convergent, then it must be bounded, that is some part of it doesn't go to infinity. This is an important result that is used over and over again in analysis. Enjoy! Other examples of limits can be seen in the
From playlist Sequences
Sparse matrices in sparse analysis - Anna Gilbert
Members' Seminar Topic: Sparse matrices in sparse analysis Speaker: Anna Gilbert Affiliation: University of Michigan; Member, School of Mathematics Date: October 28, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Thresholds in Recovery of Sparse Stochastic Block Models by Allan Sly
COLLOQUIUM THRESHOLDS IN RECOVERY OF SPARSE STOCHASTIC BLOCK MODELS SPEAKER: Allan Sly ( Princeton University) DATE: Wed, 13 February 2019, 15:00 to 16:00 VENUE: Madhava Lecture Hall, ICTS Campus, Bangalore RESOURCES ABSTRACT The stochastic block model is an inhomogeneous random gra
From playlist ICTS Colloquia
Sparse Graph Limits 2: Large Deviation Convergence Christian Borgs
Conference on Graphs and Analysis Christian Borgs June 6, 2012 More videos on http://video.ias.edu
From playlist Mathematics
Geometric Approach to Invertibility of Random Matrices (Lecture 1) by Mark Rudelson
PROGRAM: TOPICS IN HIGH DIMENSIONAL PROBABILITY ORGANIZERS: Anirban Basak (ICTS-TIFR, India) and Riddhipratim Basu (ICTS-TIFR, India) DATE & TIME: 02 January 2023 to 13 January 2023 VENUE: Ramanujan Lecture Hall This program will focus on several interconnected themes in modern probab
From playlist TOPICS IN HIGH DIMENSIONAL PROBABILITY
Complexity, Phase Transitions, and Inference by Cristopher Moore (part 2)
There is a deep analogy between statistical inference and statistical physics. I will give a friendly introduction to both of these fields. I will then discuss phase transitions in problems like community detection in networks, and clustering of sparse high-dimensional data, where if our
From playlist School on Current Frontiers in Condensed Matter Research
Biased Generator - 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
Journey trough statistical physics of constraint satisfaction and inference... by Lenka Zdeborova
26 December 2016 to 07 January 2017 VENUE: Madhava Lecture Hall, ICTS Bangalore Information theory and computational complexity have emerged as central concepts in the study of biological and physical systems, in both the classical and quantum realm. The low-energy landscape of classical
From playlist US-India Advanced Studies Institute: Classical and Quantum Information
Jeff Calder: "An intro to concentration of measure with applications to graph-based l... (Part 2/2)"
Watch part 1/2 here: https://youtu.be/Q5fB5Ldzo-g High Dimensional Hamilton-Jacobi PDEs Tutorials 2020 "An introduction to concentration of measure with applications to graph-based learning (Part 2/2)" Jeff Calder, University of Minnesota - Twin Cities Abstract: We will give a gentle in
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Nexus trimester - Henry Pfister (Duke University) 1/2
Factor Graphs, Belief Propagation, and Density Evolution - 1/2 Henry Pfister (Duke University) March 16, 2016 Abstract: The goal of this mini-course is to introduce students to marginal inference techniques for large systems of random variables defined by sparse random factor graphs. Ove
From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
Wei Kang: "Data Development and Deep Learning for HJB Equations"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop I: High Dimensional Hamilton-Jacobi Methods in Control and Differential Games "Data Development and Deep Learning for HJB Equations" Wei Kang, Naval Postgraduate School Abstract: Recent research reveals that deep learning is an effectiv
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Local Testing and Decoding of Sparse Linear Codes - Shubhangi Saraf
Shubhangi Saraf Massachusetts Institute of Technology February 22, 2011 We study the local testabilty of sparse linear codes. This problem is intimately connected to the problem of tolerant linearity testing of Boolean functions under nonuniform distributions. We give linearity tests for s
From playlist Mathematics
Math 131 092816 Continuity; Continuity and Compactness
Review definition of limit. Definition of continuity at a point; remark about isolated points; connection with limits. Composition of continuous functions. Alternate characterization of continuous functions (topological definition). Continuity and compactness: continuous image of a com
From playlist Course 7: (Rudin's) Principles of Mathematical Analysis
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
8ECM Invited Lecture: Ilaria Perugia
From playlist 8ECM Invited Lectures
Ben Adcock: Compressed sensing and high-dimensional approximation: progress and challenges
Abstract: Many problems in computational science require the approximation of a high-dimensional function from limited amounts of data. For instance, a common task in Uncertainty Quantification (UQ) involves building a surrogate model for a parametrized computational model. Complex physica
From playlist Numerical Analysis and Scientific Computing
CCSS What are truth tables and how can we create them for conditional statements
👉 Learn how to determine the truth or false of a conditional statement. A conditional statement is an if-then statement connecting a hypothesis (p) and the conclusion (q). If the hypothesis of a statement is represented by p and the conclusion is represented by q, then the conditional stat
From playlist Conditional Statements