24 citations · 95 across the 15 of their papers we have counts for
16 papers
Fitting Low-rank Models on Egocentrically Sampled Partial Networks
Angus Chan, Tianxi Li
The statistical modeling of random networks has been widely used to uncover interaction mechanisms in complex systems and to predict unobserved links in real-world networks. In man…
The non-overlapping statistical approximation to overlapping group lasso
Mingyu Qi, Tianxi Li
Group lasso is a commonly used regularization method in statistical learning in which parameters are eliminated from the model according to predefined groups. However, when the gro…
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li +1
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 advert…
Diffusion Source Identification on Networks with Statistical Confidence
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. Though th…
Network Estimation by Mixing: Adaptivity and More
Tianxi Li, Can M. Le
Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network's underlying connect…
Informative core identification in complex networks
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. The noise and bias introduced by th…
Community models for networks observed through edge nominations
Tianxi Li, Elizaveta Levina, Ji Zhu
Communities are a common and widely studied structure in networks, typically under the assumption that the network is fully and correctly observed. In practice, network data are of…
Linear regression and its inference on noisy network-linked data
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. Previou…
High-dimensional Gaussian graphical model for network-linked data
Tianxi Li, Cheng Qian, Elizaveta Levina +1
Graphical models are commonly used to represent conditional dependence relationships between variables. There are multiple methods available for exploring them from high-dimensiona…
Hierarchical community detection by recursive partitioning
Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya +4
The problem of community detection in networks is usually formulated as finding a single partition of the network into some "correct" number of communities. We argue that it is mor…
Probabilistic Rule Realization and Selection
Haizi Yu, Tianxi Li, Lav R. Varshney
Abstraction and realization are bilateral processes that are key in deriving intelligence and creativity. In many domains, the two processes are approached through rules: high-leve…
Network cross-validation by edge sampling
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. Cross-validation is a useful general tool f…
A note on the statistical view of matrix completion
Tianxi Li
A very simple interpretation of matrix completion problem is introduced based on statistical models. Combined with the well-known results from missing data analysis, such interpret…
Prediction models for network-linked data
Tianxi Li, Elizaveta Levina, Ji Zhu
Prediction algorithms typically assume the training data are independent samples, but in many modern applications samples come from individuals connected by a network. For example,…
High-dimensional Mixed Graphical Models
Jie Cheng, Tianxi Li, Elizaveta Levina +1
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 link…
High dimensional Bayesian inference for Gaussian directed acyclic graph models
Emanuel Ben-David, Tianxi Li, Helene Massam +1
We study centered Gaussian models Markov with respect to a directed acyclic graph (DAG) whose vertices have a fixed parent ordering. We construct a conjugate family on the modified…