Search Results for author:
Found 11 papers, 1 papers with code
On provable privacy vulnerabilities of graph representations
Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations.
Empirical Risk Minimization for Losses without Variance
This paper considers an empirical risk minimization problem under heavy-tailed settings, where data does not have finite variance, but only has $p$-th moment with $p \in (1, 2)$.
Copula for Instance-wise Feature Selection and Ranking
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.
A Cover Time Study of a non-Markovian Algorithm
In this work, we stand on a theoretical perspective and show that the negative feedback strategy (a count-based exploration method) is better than the naive random walk search.
On Penalization in Stochastic Multi-armed Bandits
We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes penalization into consideration.
Catoni-style Confidence Sequences under Infinite Variance
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.
Best Subset Selection with Efficient Primal-Dual Algorithm
Best subset selection is considered the `gold standard' for many sparse learning problems.
A Note on Sparse Generalized Eigenvalue Problem
The sparse generalized eigenvalue problem (SGEP) aims to find the leading eigenvector with sparsity structure.
A new look at fairness in stochastic multi-armed bandit problems
Under such a framework, we propose a hard-threshold UCB-like algorithm, which enjoys many merits including asymptotic fairness, nearly optimal regret, better tradeoff between reward and fairness.
Identifiability of Bifactor Models
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).
Statistics Theory Applications Statistics Theory
Online Estimation and Community Detection of Network Point Processes for Event Streams
To circumvent this challenge, we propose a fast online variational inference algorithm for estimating the latent structure underlying dynamic event arrivals on a network, using continuous-time point process latent network models.
