Search Results for author:
Found 26 papers, 3 papers with code
On hyperparameter tuning in general clustering problemsm
Tuning hyperparameters for unsupervised learning problems is difficult in general due to the lack of ground truth for validation.
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models
Therefore, the total computational complexity of the EGD algorithm is \emph{optimal} and exponentially cheaper than that of the GD for solving parameter estimation in non-regular statistical models while being comparable to that of the GD in regular statistical settings.
Bootstrapping the error of Oja's algorithm
We consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming principal component analysis, where the data are generated IID from some unknown distribution.
Trading off Accuracy for Speedup: Multiplier Bootstraps for Subgraph Counts
We propose a new class of multiplier bootstraps for count functionals, ranging from a fast, approximate linear bootstrap tailored to sparse, massive graphs to a quadratic bootstrap procedure that offers refined accuracy for smaller, denser graphs.
On the Theoretical Properties of the Network Jackknife
We study the properties of a leave-node-out jackknife procedure for network data.
A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers
Traditional clustering algorithms such as k-means and spectral clustering are known to perform poorly for datasets contaminated with even a small number of outliers.
A Unified Framework for Tuning Hyperparameters in Clustering Problems
In this paper, we provide a framework with provable guarantees for selecting hyperparameters in a number of distinct models.
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
Variational approximation has been widely used in large-scale Bayesian inference recently, the simplest kind of which involves imposing a mean field assumption to approximate complicated latent structures.
Hierarchical community detection by recursive partitioning
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.
Overlapping Clustering Models, and One (class) SVM to Bind Them All
People belong to multiple communities, words belong to multiple topics, and books cover multiple genres; overlapping clusters are commonplace.
Convergence of Gradient EM on Multi-component Mixture of Gaussians
In this paper, we study convergence properties of the gradient variant of Expectation-Maximization algorithm~\cite{lange1995gradient} for Gaussian Mixture Models for arbitrary number of clusters and mixing coefficients.
Estimating Mixed Memberships with Sharp Eigenvector Deviations
We consider the problem of estimating community memberships of nodes in a network, where every node is associated with a vector determining its degree of membership in each community.
Two provably consistent divide and conquer clustering algorithms for large networks
In this article, we advance divide-and-conquer strategies for solving the community detection problem in networks.
Network modelling of topological domains using Hi-C data
However, one of the drawbacks of community detection is that most methods take exchangeability of the nodes in the network for granted; whereas the nodes in this case, i. e. the positions on the chromosomes, are not exchangeable.
Applications Genomics
Provable Estimation of the Number of Blocks in Block Models
Community detection is a fundamental unsupervised learning problem for unlabeled networks which has a broad range of applications.
Convergence Analysis of Gradient EM for Multi-component Gaussian Mixture
In this paper, we study convergence properties of the gradient Expectation-Maximization algorithm \cite{lange1995gradient} for Gaussian Mixture Models for general number of clusters and mixing coefficients.
Covariate Regularized Community Detection in Sparse Graphs
In statistics, an emerging body of work has been focused on combining information from both the edges in the network and the node covariates to infer community memberships.
On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations
The problem of finding overlapping communities in networks has gained much attention recently.
On clustering network-valued data
Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the research community.
On Robustness of Kernel Clustering
Clustering is one of the most important unsupervised problems in machine learning and statistics.
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels
Link prediction and clustering are key problems for network-structureddata.
Hypothesis Testing for Automated Community Detection in Networks
Community detection in networks is a key exploratory tool with applications in a diverse set of areas, ranging from finding communities in social and biological networks to identifying link farms in the World Wide Web.
Role of normalization in spectral clustering for stochastic blockmodels
The quality of spectral clustering is closely tied to the convergence properties of these principal eigenvectors.
Active Learning for Crowd-Sourced Databases
Based on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database.
Nonparametric Link Prediction in Dynamic Networks
We propose a non-parametric link prediction algorithm for a sequence of graph snapshots over time.
Nonparametric Link Prediction in Large Scale Dynamic Networks
We propose a nonparametric approach to link prediction in large-scale dynamic networks.
