54 citations · 67 across the 14 of their papers we have counts for
14 papers
Empirical Risk Minimization for Losses without Variance
Guanhua Fang, Ping Li, Gennady Samorodnitsky
This paper considers an empirical risk minimization problem under heavy-tailed settings, where data does not have finite variance, but only has -th moment with . In…
Copula for Instance-wise Feature Selection and Ranking
Hanyu Peng, Guanhua Fang, Ping Li
Instance-wise feature selection and ranking methods can achieve a good selection of task-friendly features for each sample in the context of neural networks. However, existing appr…
A Cover Time Study of a non-Markovian Algorithm
Guanhua Fang, Gennady Samorodnitsky, Zhiqiang Xu
Given a traversal algorithm, cover time is the expected number of steps needed to visit all nodes in a given graph. A smaller cover time means a higher exploration efficiency of tr…
On Penalization in Stochastic Multi-armed Bandits
Guanhua Fang, Ping Li, Gennady Samorodnitsky
We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes penalization into consideration. Instead of directly maximizing cumulative expected re…
Catoni-style Confidence Sequences under Infinite Variance
Sujay Bhatt, Guanhua Fang, Ping Li +1
In this paper, we provide an extension of confidence sequences for settings where the variance of the data-generating distribution does not exist or is infinite. Confidence sequenc…
Best Subset Selection with Efficient Primal-Dual Algorithm
Shaogang Ren, Guanhua Fang, Ping Li
Best subset selection is considered the `gold standard' for many sparse learning problems. A variety of optimization techniques have been proposed to attack this non-convex and NP-…
Regression with Label Permutation in Generalized Linear Model
Guanhua Fang, Ping Li
The assumption that response and predictor belong to the same statistical unit may be violated in practice. Unbiased estimation and recovery of true label ordering based on unlabel…
Offline Change Detection under Contamination
Sujay Bhatt, Guanhua Fang, Ping Li
In this work, we propose a non-parametric and robust change detection algorithm to detect multiple change points in time series data under contamination. The contamination model is…
Identifiability of Bifactor Models
Guanhua Fang, Xin Xu, Jinxin Guo +2
The bifactor model and its extensions are multidimensional latent variable models, under which each item measures up to one subdimension on top of the primary dimension(s). Despite…
Online Estimation and Community Detection of Network Point Processes for Event Streams
Guanhua Fang, Owen G. Ward, Tian Zheng
A common goal in network modeling is to uncover the latent community structure present among nodes. For many real-world networks, the true connections consist of events arriving as…
Group Network Hawkes Process
Guanhua Fang, Ganggang Xu, Haochen Xu +2
In this work, we study the event occurrences of individuals interacting in a network. To characterize the dynamic interactions among the individuals, we propose a group network Haw…
A Latent Topic Model with Markovian Transition for Process Data
Haochen Xu, Guanhua Fang, Zhiliang Ying
We propose a latent topic model with a Markovian transition for process data, which consist of time-stamped events recorded in a log file. Such data are becoming more widely availa…
Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data
Guanhua Fang, Zhiliang Ying
Process data, temporally ordered categorical observations, are of recent interest due to its increasing abundance and the desire to extract useful information. A process is a colle…
On the Identifiability of Diagnostic Classification Models
Guanhua Fang, Jingchen Liu, Zhiliang Ying
This paper establishes fundamental results for statistical inference of diagnostic classification models (DCM). The results are developed at a high level of generality, applicable…