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Found 16 papers, 3 papers with code
Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity
Our statistical bounds depend on the logging policy through its mixing properties.
Contextual Unsupervised Outlier Detection in Sequences
This work proposes an unsupervised learning framework for trajectory (sequence) outlier detection that combines ranking tests with user sequence models.
Variational inference for diffusion modulated Cox processes
This paper proposes a stochastic variational inference (SVI) method for computing an approximate posterior path measure of a Cox process.
Community detection over a heterogeneous population of non-aligned networks
Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e. g., multi-view, multi-layer, temporal graphs).
Relational Pooling for Graph Representations
This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions.
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Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
We consider a simple and overarching representation for permutation-invariant functions of sequences (or multiset functions).
Flexible Mixture Modeling on Constrained Spaces
Such data are commonly found in spatial applications, such as climatology and criminology, where measurements are restricted to a geographical area.
Multi-level hypothesis testing for populations of heterogeneous networks
Current approaches to hypothesis testing for weighted networks typically requires thresholding the edge-weights, to transform the data to binary networks.
Group-Representative Functional Network Estimation from Multi-Subject fMRI Data via MRF-based Image Segmentation
We propose a novel two-phase approach to functional network estimation of multi-subject functional Magnetic Resonance Imaging (fMRI) data, which applies model-based image segmentation to determine a group-representative connectivity map.
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
Recent work has combined Stein’s method with reproducing kernel Hilbert space theory to develop nonparametric goodness-of-fit tests for un-normalized probability distributions.
Multiscale Shrinkage and Lévy Processes
The method we consider in detail, and for which numerical results are presented, is based on increments of a gamma process.
Real-Time Inference for a Gamma Process Model of Neural Spiking
With simultaneous measurements from ever increasing populations of neurons, there is a growing need for sophisticated tools to recover signals from individual neurons.
Repulsive Mixtures
One important issue that arises in using discrete mixtures is low separation in the components; in particular, different components can be introduced that are very similar and hence redundant.
MCMC for continuous-time discrete-state systems
We propose a simple and novel framework for MCMC inference in continuous-time discrete-state systems with pure jump trajectories.
Gaussian process modulated renewal processes
In our experiments, we test these on a number of synthetic and real datasets.
Spatial Normalized Gamma Processes
We propose a simple and general framework to construct dependent DPs by marginalizing and normalizing a single gamma process over an extended space.
