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Found 17 papers, 1 papers with code
$e^{\text{RPCA}}$: Robust Principal Component Analysis for Exponential Family Distributions
Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers.
MissDiff: Training Diffusion Models on Tabular Data with Missing Values
The diffusion model has shown remarkable performance in modeling data distributions and synthesizing data.
Neural Differential Recurrent Neural Network with Adaptive Time Steps
We propose an RNN-based model, called RNN-ODE-Adap, that uses a neural ODE to represent the time development of the hidden states, and we adaptively select time steps based on the steepness of changes of the data over time so as to train the model more efficiently for the "spike-like" time series.
Improving Adversarial Robustness by Contrastive Guided Diffusion Process
Among various deep generative models, the diffusion model has been shown to produce high-quality synthetic images and has achieved good performance in improving the adversarial robustness.
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains.
PERCEPT: a new online change-point detection method using topological data analysis
This yields a non-parametric, topology-aware framework which can efficiently detect online changes from high-dimensional data streams.
Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization
Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain.
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data
Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease.
Optimality of Graph Scanning Statistic for Online Community Detection
Sequential change-point detection for graphs is a fundamental problem for streaming network data types and has wide applications in social networks and power systems.
Change Point Detection
Online Community Detection
Statistics Theory
Statistics Theory
Sequential change-point detection for mutually exciting point processes over networks
We present a new CUSUM procedure for sequentially detecting change-point in the self and mutual exciting processes, a. k. a.
Uncertainty Quantification for Inferring Hawkes Networks
Multivariate Hawkes processes are commonly used to model streaming networked event data in a wide variety of applications.
Distributionally Robust Weighted $k$-Nearest Neighbors
When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier.
Convex Parameter Recovery for Interacting Marked Processes
In the proposed model, the probability of an event of a specific category to occur in a location may be influenced by past events at this and other locations.
Spectral CUSUM for Online Network Structure Change Detection
Detecting abrupt changes in the community structure of a network from noisy observations is a fundamental problem in statistics and machine learning.
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
We develop a novel computationally efficient and general framework for robust hypothesis testing.
Sequential detection of low-rank changes using extreme eigenvalues
We study the problem of detecting an abrupt change to the signal covariance matrix.
Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates
Our proof is achieved by making a connection between sequential change-point and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm.
