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Found 92 papers, 18 papers with code
Temporal Logic Point Processes
We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.
Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions.
Conformal prediction for multi-dimensional time series by ellipsoidal sets
Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound.
Detecting algorithmic bias in medical AI-models
With the growing prevalence of machine learning and artificial intelligence-based medical decision support systems, it is equally important to ensure that these systems provide patient outcomes in a fair and equitable fashion.
Mobile Internet Quality Estimation using Self-Tuning Kernel Regression
Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture.
Flow-based Distributionally Robust Optimization
We present a computationally efficient framework, called $\texttt{FlowDRO}$, for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty sets while aiming to find continuous worst-case distribution (also called the Least Favorable Distribution, LFD) and sample from it.
$e^{\text{RPCA}}$: 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.
Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance.
Assessing Electricity Service Unfairness with Transfer Counterfactual Learning
Energy justice is a growing area of interest in interdisciplinary energy research.
From CNN to Transformer: A Review of Medical Image Segmentation Models
Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment.
Deep graph kernel point processes
Point process models are widely used for continuous asynchronous event data, where each data point includes time and additional information called "marks", which can be locations, nodes, or event types.
Computing high-dimensional optimal transport by flow neural networks
Flow-based models are widely used in generative tasks, including normalizing flow, where a neural network transports from a data distribution $P$ to a normal distribution.
Transfer Learning for Causal Effect Estimation
We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data.
Generalized generalized linear models: Convex estimation and online bounds
We introduce a new computational framework for estimating parameters in generalized generalized linear models (GGLM), a class of models that extends the popular generalized linear models (GLM) to account for dependencies among observations in spatio-temporal data.
Variable Selection for Kernel Two-Sample Tests
We consider the variable selection problem for two-sample tests, aiming to select the most informative variables to distinguish samples from two groups.
Causal Structural Learning from Time Series: A Convex Optimization Approach
Structural learning, which aims to learn directed acyclic graphs (DAGs) from observational data, is foundational to causal reasoning and scientific discovery.
Causal Graph Discovery from Self and Mutually Exciting Time Series
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series.
Normalizing flow neural networks by JKO scheme
Normalizing flow is a class of deep generative models for efficient sampling and likelihood estimation, which achieves attractive performance, particularly in high dimensions.
Sequential Predictive Conformal Inference for Time Series
We present a new distribution-free conformal prediction algorithm for sequential data (e. g., time series), called the \textit{sequential predictive conformal inference} (\texttt{SPCI}).
Novelty Detection for Election Fraud: A Case Study with Agent-Based Simulation Data
We generate a clean election results dataset without fraud as well as datasets with varying degrees of fraud.
Online Kernel CUSUM for Change-Point Detection
We present a computationally efficient online kernel Cumulative Sum (CUSUM) method for change-point detection that utilizes the maximum over a set of kernel statistics to account for the unknown change-point location.
Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes
In this paper, we attempt to detect an inflection or change-point resulting from the Covid-19 pandemic on supply chain data received from a large furniture company.
Spatio-temporal point processes with deep non-stationary kernels
Another popular type of deep model for point process data is based on representing the influence kernel (rather than the intensity function) by neural networks.
Neural network-based CUSUM for online change-point detection
Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning.
Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes
Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have elucidated prodromes (i. e., event chains) that can be used to identify and intercept the adverse event early in its course.
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.
Neural Stein critics with staged $L^2$-regularization
In this paper, we investigate the role of $L^2$ regularization in training a neural network Stein critic so as to distinguish between data sampled from an unknown probability distribution and a nominal model distribution.
Conformal prediction set for time-series
When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods.
Invertible Neural Networks for Graph Prediction
The proposed model consists of an invertible sub-network that maps one-to-one from data to an intermediate encoded feature, which allows forward prediction by a linear classification sub-network as well as efficient generation from output labels via a parametric mixture model.
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks
Based on the approximation theory of neural networks, we show that the neural network IPM test has the type-II risk in the order of $n^{-(s+\beta)/d}$, which is in the same order of the type-II risk as the H\"older IPM test.
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.
An alternative approach to train neural networks using monotone variational inequality
We propose an alternative approach to neural network training using the monotone vector field, an idea inspired by the seminal work of Juditsky and Nemirovski [Juditsky & Nemirovsky, 2019] developed originally to solve parameter estimation problems for generalized linear models (GLM) by reducing the original non-convex problem to a convex problem of solving a monotone variational inequality (VI).
A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets
Hypothesis testing for small-sample scenarios is a practically important problem.
Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation
We present a method to capture groupings of similar calls and determine their relative spatial distribution from a collection of crime record narratives.
Learning Sinkhorn divergences for supervised change point detection
We present a novel change point detection framework that uses true change point instances as supervision for learning a ground metric such that Sinkhorn divergences can be then used in two-sample tests on sliding windows to detect change points in an online manner.
Sinkhorn Distributionally Robust Optimization
We study distributionally robust optimization (DRO) with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization.
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.
Neural Spectral Marked Point Processes
In this paper, we introduce a novel and general neural network-based non-stationary influence kernel with high expressiveness for handling complex discrete events data while providing theoretical performance guarantees.
Neural Tangent Kernel Maximum Mean Discrepancy
We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by identifying a new connection between neural tangent kernel (NTK) and MMD.
Causal Graph Discovery from Self and Mutually Exciting Time Series
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series.
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.
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data
We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies.
Kernel Two-Sample Tests for Manifold Data
Specifically, when data densities $p$ and $q$ are supported on a $d$-dimensional sub-manifold ${M}$ embedded in an $m$-dimensional space and are H\"older with order $\beta$ (up to 2) on ${M}$, we prove a guarantee of the test power for finite sample size $n$ that exceeds a threshold depending on $d$, $\beta$, and $\Delta_2$ the squared $L^2$-divergence between $p$ and $q$ on the manifold, and with a properly chosen kernel bandwidth $\gamma$.
Signal Processing Challenges and Examples for {\it in-situ} Transmission Electron Microscopy
Transmission Electron Microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry.
Data-Driven Optimization for Atlanta Police Zone Design
By analyzing data before and after the zone redesign, we show that the new design has reduced the response time to high priority 911 calls by 5. 8\% and the imbalance of police workload among different zones by 43\%.
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.
Balanced Districting on Grid Graphs with Provable Compactness and Contiguity
Given a graph $G = (V, E)$ with vertex weights $w(v)$ and a desired number of parts $k$, the goal in graph partitioning problems is to partition the vertex set V into parts $V_1,\ldots, V_k$.
graph partitioning
Data Structures and Algorithms
Combinatorics
Optimization and Control
Solar Radiation Ramping Events Modeling Using Spatio-temporal Point Processes
Modeling and predicting solar events, particularly the solar ramping event, is critical for improving situational awareness for solar power generation systems.
Inferring serial correlation with dynamic backgrounds
Sequential data with serial correlation and an unknown, unstructured, and dynamic background is ubiquitous in neuroscience, psychology, and econometrics.
Statistics Theory Methodology Statistics Theory
Bayesian Uncertainty Quantification for Low-Rank Matrix Completion
We consider the problem of uncertainty quantification for an unknown low-rank matrix $\mathbf{X}$, given a partial and noisy observation of its entries.
Decision Making
Low-Rank Matrix Completion
Methodology
Tensor Kernel Recovery for Spatio-Temporal Hawkes Processes
We estimate the general influence functions for spatio-temporal Hawkes processes using a tensor recovery approach by formulating the location dependent influence function that captures the influence of historical events as a tensor kernel.
Two-sample Test using Projected Wasserstein Distance
We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution.
Conformal prediction interval for dynamic time-series
We develop a method to construct distribution-free prediction intervals for dynamic time-series, called \Verb|EnbPI| that wraps around any bootstrap ensemble estimator to construct sequential prediction intervals.
Goodness-of-Fit Test for Mismatched Self-Exciting Processes
Recently there have been many research efforts in developing generative models for self-exciting point processes, partly due to their broad applicability for real-world applications.
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.
Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling
We present a novel framework for modeling traffic congestion events over road networks.
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.
Deep Fourier Kernel for Self-Attentive Point Processes
We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures.
Sequential Adversarial Anomaly Detection for One-Class Event Data
We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator.
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.
Goodness-of-fit tests on manifolds
We develop a general theory for the goodness-of-fit test to non-linear models.
Imitation Learning of Neural Spatio-Temporal Point Processes
We present a novel Neural Embedding Spatio-Temporal (NEST) point process model for spatio-temporal discrete event data and develop an efficient imitation learning (a type of reinforcement learning) based approach for model fitting.
Deep Zero-Shot Learning for Scene Sketch
We introduce a novel problem of scene sketch zero-shot learning (SSZSL), which is a challenging task, since (i) different from photo, the gap between common semantic domain (e. g., word vector) and sketch is too huge to exploit common semantic knowledge as the bridge for knowledge transfer, and (ii) compared with single-object sketch, more expressive feature representation for scene sketch is required to accommodate its high-level of abstraction and complexity.
Spatial-Temporal-Textual Point Processes for Crime Linkage Detection
We propose a new statistical modeling framework for {\it spatio-temporal-textual} data and demonstrate its usage on crime linkage detection.
Learning Transformation Synchronization
Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network.
Learning Temporal Point Processes via Reinforcement Learning
Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.
Crime Event Embedding with Unsupervised Feature Selection
Using numerical experiments on a large-scale crime dataset, we show that our regularized RBMs can achieve better event embedding and the selected features are highly interpretable from human understanding.
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
We develop a novel computationally efficient and general framework for robust hypothesis testing.
Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit
Multi-armed bandit (MAB) is a class of online learning problems where a learning agent aims to maximize its expected cumulative reward while repeatedly selecting to pull arms with unknown reward distributions.
Matrix completion with deterministic pattern - a geometric perspective
We argue that this condition is necessary for local stability of MRMC solutions, and we show that the condition is generic using the characteristic rank.
Crime incidents embedding using restricted Boltzmann machines
We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM).
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.
Dynamic change-point detection using similarity networks
From a sequence of similarity networks, with edges representing certain similarity measures between nodes, we are interested in detecting a change-point which changes the statistical property of the networks.
Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits
We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence.
Sequential Low-Rank Change Detection
The premise is that when the sketching matrix is a random Gaussian matrix, and the dimension of the sketching vector is sufficiently large, the rank of sample covariance matrix for these sketches equals the rank of the original sample covariance matrix with high probability.
Detecting weak changes in dynamic events over networks
Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics.
M-Statistic for Kernel Change-Point Detection
Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning.
Online Supervised Subspace Tracking
The optimization problem for OSDR is non-convex and hard to analyze in general; we provide convergence analysis of OSDR in a simple linear regression setting.
Sequential Information Guided Sensing
We study the value of information in sequential compressed sensing by characterizing the performance of sequential information guided sensing in practical scenarios when information is inaccurate.
Multi-Sensor Slope Change Detection
Observations are assumed to be initially normal random variables with known constant means and variances.
Scan $B$-Statistic for Kernel Change-Point Detection
A novel theoretical result of the paper is the characterization of the tail probability of these statistics using the change-of-measure technique, which focuses on characterizing the tail of the detection statistics rather than obtaining its asymptotic distribution under the null distribution.
Categorical Matrix Completion
We recover a low-rank matrix $X$ by maximizing the likelihood ratio with a constraint on the nuclear norm of $X$, and the observations are mapped from entries of $X$ through multiple link functions.
Sketching for Sequential Change-Point Detection
We study sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics.
Poisson Matrix Recovery and Completion
We extend the theory of low-rank matrix recovery and completion to the case when Poisson observations for a linear combination or a subset of the entries of a matrix are available, which arises in various applications with count data.
Poisson Matrix Completion
We extend the theory of matrix completion to the case where we make Poisson observations for a subset of entries of a low-rank matrix.
Sequential Sensing with Model Mismatch
We characterize the performance of sequential information guided sensing, Info-Greedy Sensing, when there is a mismatch between the true signal model and the assumed model, which may be a sample estimate.
Sequential Changepoint Approach for Online Community Detection
Finally, numerical examples show that the mixture and the H-Mix methods can both detect a community quickly with a lower complexity than the ES method.
Fast Algorithm for Low-rank matrix recovery in Poisson noise
This paper describes a fast algorithm for recovering low-rank matrices from their linear measurements contaminated with Poisson noise: the Poisson noise Maximum Likelihood Singular Value thresholding (PMLSV) algorithm.
Info-Greedy sequential adaptive compressed sensing
We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements.
