no code implementations • 9 Mar 2023 • Angus Chan, Tianxi Li
This method is based on graph spectral properties and is computationally efficient for large-scale networks.
no code implementations • 16 Nov 2022 • Mingyu Qi, Tianxi Li
Thanks to the separability, the computation of regularization based on our penalty is substantially faster than that of the overlapping group lasso, especially for large-scale and high-dimensional problems.
no code implementations • 2 Jun 2022 • Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li, Haifeng Xu
Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e. g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms.
1 code implementation • 9 Jun 2021 • Quinlan Dawkins, Tianxi Li, Haifeng Xu
Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including rumor controlling and virus identification.
no code implementations • 5 Jun 2021 • Tianxi Li, Can M. Le
While many methods have been proposed to address this problem in recent years, they usually assume that the true model belongs to a known class, which is not verifiable in most real-world applications.
1 code implementation • 16 Jan 2021 • Ruizhong Miao, Tianxi Li
In network analysis, the core structure of modeling interest is usually hidden in a larger network in which most structures are not informative.
no code implementations • 9 Aug 2020 • Tianxi Li, Elizaveta Levina, Ji Zhu
We propose a general model for a class of network sampling mechanisms based on recording edges via querying nodes, designed to improve community detection for network data collected in this fashion.
no code implementations • 1 Jul 2020 • Can M. Le, Tianxi Li
Linear regression on network-linked observations has been an essential tool in modeling the relationship between response and covariates with additional network structures.
Methodology
1 code implementation • 4 Jul 2019 • Tianxi Li, Cheng Qian, Elizaveta Levina, Ji Zhu
Graphical models are commonly used to represent conditional dependence relationships between variables.
no code implementations • 2 Oct 2018 • Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter J. Bickel, Elizaveta Levina
This can be done with a simple top-down recursive partitioning algorithm, starting with a single community and separating the nodes into two communities by spectral clustering repeatedly, until a stopping rule suggests there are no further communities.
no code implementations • NeurIPS 2017 • Haizi Yu, Tianxi Li, Lav R. Varshney
Abstraction and realization are bilateral processes that are key in deriving intelligence and creativity.
no code implementations • 14 Dec 2016 • Tianxi Li, Elizaveta Levina, Ji Zhu
While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem.
no code implementations • 10 May 2016 • Tianxi Li
A very simple interpretation of matrix completion problem is introduced based on statistical models.
no code implementations • 9 Apr 2013 • Jie Cheng, Tianxi Li, Elizaveta Levina, Ji Zhu
While graphical models for continuous data (Gaussian graphical models) and discrete data (Ising models) have been extensively studied, there is little work on graphical models linking both continuous and discrete variables (mixed data), which are common in many scientific applications.