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Found 26 papers, 9 papers with code
Nested stochastic block model for simultaneously clustering networks and nodes
We introduce the nested stochastic block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network.
A Bayesian sparse factor model with adaptive posterior concentration
In this paper, we propose a new Bayesian inference method for a high-dimensional sparse factor model that allows both the factor dimensionality and the sparse structure of the loading matrix to be inferred.
Machine Learning and the Future of Bayesian Computation
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information.
A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks
Bayesian methods for GOF can be appealing due to their ability to incorporate expert knowledge through prior distributions.
Intrinsic and extrinsic deep learning on manifolds
We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds.
Extrinsic Bayesian Optimizations on Manifolds
We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds.
Bayesian community detection for networks with covariates
The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms.
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity
Adversarial examples can easily degrade the classification performance in neural networks.
Adaptive variational Bayes: Optimality, computation and applications
It turns out that this combined variational posterior is the closest member to the posterior over the entire model in a predefined family of approximating distributions.
Training Graph Neural Networks by Graphon Estimation
In this work, we propose to train a graph neural network via resampling from a graphon estimate obtained from the underlying network data.
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models
In the considered model, a usual likelihood approach can fail to estimate the target distribution consistently due to the singularity.
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds
One of the key challenges for optimization on manifolds is the difficulty of verifying the complexity of the objective function, e. g., whether the objective function is convex or non-convex, and the degree of non-convexity.
Community detection, pattern recognition, and hypergraph-based learning: approaches using metric geometry and persistent homology
Also based on the topological space structure of hypergraph data introduced in our paper, we introduce a modified nearest neighbors methods which is a generalization of the classical nearest neighbors methods from machine learning.
Weight Prediction for Variants of Weighted Directed Networks
We introduce, for the first time, a metric geometry approach to studying edge weight prediction in WDNs.
Neural Time-Dependent Partial Differential Equation
Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence-to-sequence learning (Seq2Seq) framework called Neural-PDE, which allows one to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the solutions in next $n$ time steps.
Neural-PDE: A RNN based neural network for solving time dependent PDEs
Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering.
Optimization of Graph Neural Networks with Natural Gradient Descent
In this work, we propose to employ information-geometric tools to optimize a graph neural network architecture such as the graph convolutional networks.
Ranked #1 on
Node Classification
on Cora
Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome
While the study of a single network is well-established, technological advances now allow for the collection of multiple networks with relative ease.
Applications
Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks
Moreover, our model automatically picks up the necessary number of communities at each layer (as validated by real data examples).
Exact slice sampler for Hierarchical Dirichlet Processes
We propose an exact slice sampler for Hierarchical Dirichlet process (HDP) and its associated mixture models (Teh et al., 2006).
Communication Efficient Parallel Algorithms for Optimization on Manifolds
Our work aims to fill a critical gap in the literature by generalizing parallel inference algorithms to optimization on manifolds.
Network Distance Based on Laplacian Flows on Graphs
In this paper, we focus on proposing such a distance between network objects.
Intrinsic Gaussian processes on complex constrained domains
in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.
Robust and Parallel Bayesian Model Selection
Effective and accurate model selection is an important problem in modern data analysis.
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.
Robust and Scalable Bayes via a Median of Subset Posterior Measures
We propose a novel approach to Bayesian analysis that is provably robust to outliers in the data and often has computational advantages over standard methods.
