3 citations · 6 across the 11 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-…