Mathematics - Microsoft Research

Publication

An Lp theory of sparse graph convergence II: LD convergence, quotients, and right convergence

Christian Borgs, Jennifer Chayes, Henry Cohn, Yufei Zhao

February 2018, Vol 46: pp. 337-396

February 2018 | The Annals of Probability edition edition

Publication

Nonlinear motion of an oscillating bubble immersed in a magnetic fluid

Sara Malvar, R. G. Gontijo, F. R. Cunha

Journal of Engineering Mathematics | January 2018, Vol 108(1): pp. 143-170

Microsoft Research Blog

Learn Artificial Intelligence Skills via Residency at Microsoft

January 30, 2018 | Christopher Bishop

Harness Machine Learning to Improve People’s Lives At Microsoft, we are committed to leveraging the power of Artificial Intelligence (AI) to benefit people and greater society. Advances in AI can be applied to address some…

Publication

On self-concordant barriers for generalized power cones

Scott Roy, Lin Xiao (lixiao)

MSR-TR-2018-3 | January 2018

Project

Publication

Theoretical Aspects of Symbolic Automata

Hellis Tamm, Margus Veanes

44th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM’18) | January 2018

Project

Video

Quadratic Assignment on Different Data Models

January 23, 2018 | Soledad Villar

Quadratic assignment is a very general problem in theoretical computer science. It includes graph matching, the traveling salesman problem, and the Gromov-Hausdorff distance between finite metric spaces as particular cases. Quadratic assignment is in general…

Quadratic Assignment on Different Data Models

0:59:25

Publication

37th {IARCS} Annual Conference on Foundations of Software Technology and Theoretical Computer Science, {FSTTCS} 2017, December 11-15, 2017, Kanpur, India

Satya Lokam, R. Ramanujam

Published by Schloss Dagstuhl – Leibniz-Zentrum f{\"{u}}r Informatik | January 2018, Vol 93

Editor(s): Satya V. Lokam and R. Ramanujam

ISBN: 978-3-95977-055-2

Video

Understanding Over-parametrization Through Matrix Sensing

January 4, 2018 | Yuanzhi Li

We study the problem of recovering a low-rank matrix from linear measurements using an over-parameterized model. We show that the gradient descent process on the square loss function, starting from a small initialization, can converge…

Understanding Over-parametrization Through Matrix Sensing

1:17:46

Publication

On Automatically Proving the Correctness of math.h Implementations

Wonyeol Lee, Rahul Sharma, Alex Aiken

Principles of Programming Languages 2018 (POPL 2018) | January 2018

Publication

SECURITY OF HOMOMORPHIC ENCRYPTION

Melissa Chase, Hao Chen, Jintai Ding, Shafi Goldwasser, Sergey Gorbunov, Jeffrey Hoffstein, Kristin Lauter, Satya Lokam, Dustin Moody, Travis Morrison, Amit Sahai, Vinod Vaikuntanathan

January 2018