no code implementations • 15 May 2024 • Siwei Wang, Yifei Shen, Shi Feng, Haoran Sun, Shang-Hua Teng, Wei Chen
In this paper, we present the findings of our Project ALPINE which stands for ``Autoregressive Learning for Planning In NEtworks."
no code implementations • 15 Feb 2024 • Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
Furthermore, the learnability of such problems can fail to be a property of finite character: informally, it cannot be detected by examining finite projections of the problem.
no code implementations • 24 Sep 2023 • Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
We demonstrate that an agnostic version of the Hall complexity again characterizes error rates exactly, and exhibit an optimal learner using maximum entropy programs.
no code implementations • 7 Nov 2020 • Kyle Burke, Matthew Ferland, Shang-Hua Teng
The beauty of quantum games-succinct in representation, rich in structures, explosive in complexity, dazzling for visualization, and sophisticated for strategic reasoning-has drawn us to play concrete games full of subtleties and to characterize abstract properties pertinent to complexity consequence.
no code implementations • 25 Aug 2017 • Shang-Hua Teng
In the age of social and information networks, I will then turn the discussion from geometric structures to network structures, attempting to take a humble step towards the holy grail of network science, that is to understand the network essence that underlies the observed sparse-and-multifaceted network data.
no code implementations • 12 Feb 2015 • Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng
Our work is particularly motivated by the algorithmic problems for speeding up the classic Newton's method in applications such as computing the inverse square-root of the precision matrix of a Gaussian random field, as well as computing the $q$th-root transition (for $q\geq1$) in a time-reversible Markov model.
no code implementations • 20 Oct 2014 • Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng
random samples for $n$-dimensional Gaussian random fields with SDDM precision matrices.
no code implementations • 9 Aug 2014 • Konstantin Voevodski, Maria-Florina Balcan, Heiko Roglin, Shang-Hua Teng, Yu Xia
Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points.
no code implementations • 19 Nov 2001 • Daniel A. Spielman, Shang-Hua Teng
We measure this performance in terms of both the input size and the magnitude of the perturbations.
Data Structures and Algorithms G.1.6