Search Results for author: Yao Xie

Found 97 papers, 20 papers with code

Temporal Logic Point Processes

no code implementations ICML 2020 Shuang Li, Lu Wang, Ruizhi Zhang, xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song

We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.

Point Processes

Nonlinear time-series embedding by monotone variational inequality

no code implementations11 Jun 2024 Jonathan Y. Zhou, Yao Xie

In the wild, we often encounter collections of sequential data such as electrocardiograms, motion capture, genomes, and natural language, and sequences may be multichannel or symbolic with nonlinear dynamics.

Clustering Time Series

Transformer Conformal Prediction for Time Series

1 code implementation8 Jun 2024 Junghwan Lee, Chen Xu, Yao Xie

We present a conformal prediction method for time series using the Transformer architecture to capture long-memory and long-range dependencies.

Conformal Prediction Decoder +1

Learning Cellular Network Connection Quality with Conformal

no code implementations4 Jun 2024 Hanyang Jiang, Elizabeth Belding, Ellen Zegure, Yao Xie

To improve the reliability of this map, we introduce a novel conformal prediction technique to build an uncertainty map.

Conformal Prediction Uncertainty Quantification

Kernel-based optimally weighted conformal prediction intervals

no code implementations27 May 2024 JongHyeok Lee, Chen Xu, Yao Xie

Conformal prediction has been a popular distribution-free framework for uncertainty quantification.

Conformal Prediction Prediction Intervals +3

Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances

no code implementations24 May 2024 Jie Wang, March Boedihardjo, Yao Xie

Optimal transport has been very successful for various machine learning tasks; however, it is known to suffer from the curse of dimensionality.

Dimensionality Reduction Two-sample testing

Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets

no code implementations21 Mar 2024 Jie Wang, Rui Gao, Yao Xie

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.

Computational Efficiency

Conformal prediction for multi-dimensional time series by ellipsoidal sets

1 code implementation6 Mar 2024 Chen Xu, Hanyang Jiang, Yao Xie

Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound.

Conformal Prediction Prediction Intervals +3

Detecting algorithmic bias in medical-AI models using trees

no code implementations5 Dec 2023 Jeffrey Smith, Andre Holder, Rishikesan Kamaleswaran, Yao Xie

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

no code implementations4 Nov 2023 Hanyang Jiang, Henry Shaowu Yuchi, Elizabeth Belding, Ellen Zegura, Yao Xie

Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture.

regression Weather Forecasting

Flow-based Distributionally Robust Optimization

1 code implementation30 Oct 2023 Chen Xu, JongHyeok Lee, Xiuyuan Cheng, Yao Xie

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

no code implementations30 Oct 2023 Xiaojun Zheng, Simon Mak, Liyan Xie, Yao Xie

Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers.

Defect Detection

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space

1 code implementation26 Oct 2023 Xiuyuan Cheng, Jianfeng Lu, Yixin Tan, Yao Xie

Leveraging the exponential convergence of the proximal gradient descent (GD) in Wasserstein space, we prove the Kullback-Leibler (KL) guarantee of data generation by a JKO flow model to be $O(\varepsilon^2)$ when using $N \lesssim \log (1/\varepsilon)$ many JKO steps ($N$ Residual Blocks in the flow) where $\varepsilon $ is the error in the per-step first-order condition.

From CNN to Transformer: A Review of Medical Image Segmentation Models

no code implementations10 Aug 2023 Wenjian Yao, Jiajun Bai, Wei Liao, YuHeng Chen, Mengjuan Liu, Yao Xie

Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment.

Image Segmentation Medical Image Segmentation +2

Deep graph kernel point processes

no code implementations20 Jun 2023 Zheng Dong, Matthew Repasky, Xiuyuan Cheng, Yao Xie

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.

Point Processes

Computing high-dimensional optimal transport by flow neural networks

no code implementations19 May 2023 Chen Xu, Xiuyuan Cheng, Yao Xie

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.

Density Ratio Estimation Image-to-Image Translation +1

Transfer Learning for Causal Effect Estimation

no code implementations16 May 2023 Song Wei, Hanyu Zhang, Ronald Moore, Rishikesan Kamaleswaran, Yao Xie

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.

regression Transfer Learning

Generalized generalized linear models: Convex estimation and online bounds

no code implementations26 Apr 2023 Anatoli Juditsky, Arkadi Nemirovski, Yao Xie, Chen Xu

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

no code implementations15 Feb 2023 Jie Wang, Santanu S. Dey, Yao Xie

We consider the variable selection problem for two-sample tests, aiming to select the most informative variables to distinguish samples from two groups.

Variable Selection Vocal Bursts Valence Prediction

Causal Structural Learning from Time Series: A Convex Optimization Approach

no code implementations26 Jan 2023 Song Wei, Yao Xie

Structural learning, which aims to learn directed acyclic graphs (DAGs) from observational data, is foundational to causal reasoning and scientific discovery.

Time Series Time Series Analysis

Causal Graph Discovery from Self and Mutually Exciting Time Series

no code implementations26 Jan 2023 Song Wei, Yao Xie, Christopher S. Josef, Rishikesan Kamaleswaran

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.

Causal Discovery Time Series +1

Normalizing flow neural networks by JKO scheme

1 code implementation NeurIPS 2023 Chen Xu, Xiuyuan Cheng, Yao Xie

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

1 code implementation7 Dec 2022 Chen Xu, Yao Xie

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}).

Conformal Prediction quantile regression +3

Online Kernel CUSUM for Change-Point Detection

1 code implementation28 Nov 2022 Song Wei, Yao Xie

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.

Change Point Detection

Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes

no code implementations22 Nov 2022 Khurram Yamin, Haoyun Wang, Benoit Montreuil, Yao Xie

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.

Change Point Detection

Spatio-temporal point processes with deep non-stationary kernels

no code implementations21 Nov 2022 Zheng Dong, Xiuyuan Cheng, Yao Xie

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.

Computational Efficiency Point Processes

Neural network-based CUSUM for online change-point detection

no code implementations31 Oct 2022 Tingnan Gong, Junghwan Lee, Xiuyuan Cheng, Yao Xie

Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning.

Change Point Detection Computational Efficiency

Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes

1 code implementation9 Sep 2022 Song Wei, Yao Xie, Christopher S. Josef, Rishikesan Kamaleswaran

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

no code implementations11 Jul 2022 Jingge Wang, Liyan Xie, Yao Xie, Shao-Lun Huang, Yang Li

Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains.

Domain Generalization Rotated MNIST +1

Neural Stein critics with staged $L^2$-regularization

1 code implementation7 Jul 2022 Matthew Repasky, Xiuyuan Cheng, Yao Xie

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

2 code implementations15 Jun 2022 Chen Xu, Yao Xie

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.

Conformal Prediction Prediction Intervals +4

Invertible Neural Networks for Graph Prediction

1 code implementation2 Jun 2022 Chen Xu, Xiuyuan Cheng, Yao Xie

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.

Anomaly Detection Graph Neural Network

A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks

no code implementations4 May 2022 Jie Wang, Minshuo Chen, Tuo Zhao, Wenjing Liao, Yao Xie

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

no code implementations8 Mar 2022 Xiaojun Zheng, Simon Mak, Liyan Xie, Yao Xie

This yields a non-parametric, topology-aware framework which can efficiently detect online changes from high-dimensional data streams.

Change Point Detection Topological Data Analysis

An alternative approach to train neural networks using monotone variational inequality

1 code implementation17 Feb 2022 Chen Xu, Xiuyuan Cheng, Yao Xie

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

no code implementations9 Feb 2022 Jie Wang, Yao Xie

Hypothesis testing for small-sample scenarios is a practically important problem.

Learning Sinkhorn divergences for supervised change point detection

no code implementations8 Feb 2022 Nauman Ahad, Eva L. Dyer, Keith B. Hengen, Yao Xie, Mark A. Davenport

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.

Change Detection Change Point Detection +1

Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation

no code implementations8 Feb 2022 Jonathan Zhou, Sarah Huestis-Mitchell, Xiuyuan Cheng, Yao Xie

We present a method to capture groupings of similar calls and determine their relative spatial distribution from a collection of crime record narratives.

Density Estimation Density Ratio Estimation

Sinkhorn Distributionally Robust Optimization

1 code implementation24 Sep 2021 Jie Wang, Rui Gao, Yao Xie

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

no code implementations8 Sep 2021 Jingge Wang, Yang Li, Liyan Xie, Yao Xie

Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain.

Domain Generalization

Neural Spectral Marked Point Processes

1 code implementation ICLR 2022 Shixiang Zhu, Haoyun Wang, Zheng Dong, Xiuyuan Cheng, Yao Xie

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.

Point Processes

Neural Tangent Kernel Maximum Mean Discrepancy

1 code implementation NeurIPS 2021 Xiuyuan Cheng, Yao Xie

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

no code implementations4 Jun 2021 Song Wei, Yao Xie, Christopher S. Josef, Rishikesan Kamaleswaran

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.

Causal Discovery feature selection +2

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

no code implementations31 May 2021 Shixiang Zhu, Alexander Bukharin, Liyan Xie, Khurram Yamin, Shihao Yang, Pinar Keskinocak, Yao Xie

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

1 code implementation25 May 2021 Chen Xu, Yao Xie

We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies.

Conformal Prediction Prediction Intervals +2

Kernel Two-Sample Tests for Manifold Data

1 code implementation7 May 2021 Xiuyuan Cheng, Yao Xie

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 $\gamma$.

Vocal Bursts Valence Prediction

Signal Processing Challenges and Examples for {\it in-situ} Transmission Electron Microscopy

no code implementations18 Apr 2021 Josh Kacher, Yao Xie, Sven P. Voigt, Shixiang Zhu, Henry Yuchi, Jordan Key, Surya R. Kalidindi

Transmission Electron Microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry.

Data-Driven Optimization for Atlanta Police Zone Design

no code implementations30 Mar 2021 Shixiang Zhu, He Wang, Yao Xie

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

no code implementations11 Feb 2021 Liyan Xie, Yao Xie

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

no code implementations10 Feb 2021 Haoyun Wang, Liyan Xie, Yao Xie, Alex Cuozzo, Simon Mak

We present a new CUSUM procedure for sequentially detecting change-point in the self and mutual exciting processes, a. k. a.

Change Detection Change Point Detection +1

Balanced Districting on Grid Graphs with Provable Compactness and Contiguity

no code implementations9 Feb 2021 Cyrus Hettle, Shixiang Zhu, Swati Gupta, Yao Xie

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

no code implementations27 Jan 2021 Minghe Zhang, Chen Xu, Andy Sun, Feng Qiu, Yao Xie

Modeling and predicting solar events, particularly the solar ramping event, is critical for improving situational awareness for solar power generation systems.

Point Processes Position

Inferring serial correlation with dynamic backgrounds

no code implementations26 Jan 2021 Song Wei, Yao Xie, Dobromir Rahnev

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

no code implementations5 Jan 2021 Henry Shaowu Yuchi, Simon Mak, Yao Xie

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

no code implementations24 Nov 2020 Heejune Sheen, Xiaonan Zhu, Yao Xie

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

no code implementations22 Oct 2020 Jie Wang, Rui Gao, Yao Xie

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

2 code implementations18 Oct 2020 Chen Xu, Yao Xie

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.

Conformal Prediction Ensemble Learning +5

Goodness-of-Fit Test for Mismatched Self-Exciting Processes

no code implementations16 Jun 2020 Song Wei, Shixiang Zhu, Minghe Zhang, Yao Xie

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.

Point Processes

Uncertainty Quantification for Inferring Hawkes Networks

no code implementations NeurIPS 2020 Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, Yao Xie

Multivariate Hawkes processes are commonly used to model streaming networked event data in a wide variety of applications.

Uncertainty Quantification

Distributionally Robust Weighted $k$-Nearest Neighbors

no code implementations7 Jun 2020 Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, Yao Xie

When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier.

Few-Shot Learning General Classification +1

Convex Parameter Recovery for Interacting Marked Processes

no code implementations29 Mar 2020 Anatoli Juditsky, Arkadi Nemirovski, Liyan Xie, Yao Xie

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.

Point Processes

Deep Fourier Kernel for Self-Attentive Point Processes

no code implementations17 Feb 2020 Shixiang Zhu, Minghe Zhang, Ruyi Ding, Yao Xie

We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures.

Deep Attention Point Processes

Sequential Adversarial Anomaly Detection for One-Class Event Data

no code implementations21 Oct 2019 Shixiang Zhu, Henry Shaowu Yuchi, Minghe Zhang, Yao Xie

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.

Anomaly Detection Point Processes

Spectral CUSUM for Online Network Structure Change Detection

no code implementations20 Oct 2019 Minghe Zhang, Liyan Xie, Yao Xie

Detecting abrupt changes in the community structure of a network from noisy observations is a fundamental problem in statistics and machine learning.

Change Detection Event Detection +1

Goodness-of-fit tests on manifolds

no code implementations11 Sep 2019 Alexander Shapiro, Yao Xie, Rui Zhang

We develop a general theory for the goodness-of-fit test to non-linear models.

Imitation Learning of Neural Spatio-Temporal Point Processes

1 code implementation13 Jun 2019 Shixiang Zhu, Shuang Li, Zhigang Peng, Yao Xie

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.

Computational Efficiency Imitation Learning +1

Deep Zero-Shot Learning for Scene Sketch

no code implementations11 May 2019 Yao Xie, Peng Xu, Zhanyu Ma

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.

Transfer Learning Zero-Shot Learning

Spatial-Temporal-Textual Point Processes for Crime Linkage Detection

no code implementations1 Feb 2019 Shixiang Zhu, Yao Xie

We propose a new statistical modeling framework for {\it spatio-temporal-textual} data and demonstrate its usage on crime linkage detection.

Point Processes

Learning Transformation Synchronization

1 code implementation CVPR 2019 Xiangru Huang, Zhenxiao Liang, Xiaowei Zhou, Yao Xie, Leonidas Guibas, Qi-Xing Huang

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

no code implementations NeurIPS 2018 Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song

Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.

Point Processes reinforcement-learning +1

Crime Event Embedding with Unsupervised Feature Selection

no code implementations15 Jun 2018 Shixiang Zhu, Yao Xie

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.

feature selection

Robust Hypothesis Testing Using Wasserstein Uncertainty Sets

no code implementations NeurIPS 2018 Rui Gao, Liyan Xie, Yao Xie, Huan Xu

We develop a novel computationally efficient and general framework for robust hypothesis testing.

Two-sample testing

Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit

no code implementations11 Feb 2018 Yang Cao, Zheng Wen, Branislav Kveton, Yao Xie

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.

Change Detection

Matrix completion with deterministic pattern - a geometric perspective

no code implementations31 Jan 2018 Alexander Shapiro, Yao Xie, Rui Zhang

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.

Matrix Completion

Crime incidents embedding using restricted Boltzmann machines

no code implementations28 Oct 2017 Shixiang Zhu, Yao Xie

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

no code implementations15 Jun 2017 Liyan Xie, Yao Xie

We study the problem of detecting an abrupt change to the signal covariance matrix.

Change Point Detection

Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates

no code implementations19 May 2017 Yang Cao, Liyan Xie, Yao Xie, Huan Xu

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.

Change Point Detection

Dynamic change-point detection using similarity networks

no code implementations5 Dec 2016 Shanshan Cao, Yao Xie

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.

Change Detection Change Point Detection +1

Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits

no code implementations14 Oct 2016 Shuang Li, Yao Xie, Le Song

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.

Change Point Detection Computational Efficiency

Sequential Low-Rank Change Detection

no code implementations3 Oct 2016 Yao Xie, Lee Seversky

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.

Change Detection Dimensionality Reduction

Detecting weak changes in dynamic events over networks

no code implementations29 Mar 2016 Shuang Li, Yao Xie, Mehrdad Farajtabar, Apurv Verma, Le Song

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.

Change Point Detection Point Processes

M-Statistic for Kernel Change-Point Detection

no code implementations NeurIPS 2015 Shuang Li, Yao Xie, Hanjun Dai, Le Song

Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning.

Change Point Detection

Multi-Sensor Slope Change Detection

no code implementations1 Sep 2015 Yang Cao, Yao Xie, Nagi Gebraeel

Observations are assumed to be initially normal random variables with known constant means and variances.

Change Detection

Online Supervised Subspace Tracking

no code implementations1 Sep 2015 Yao Xie, Ruiyang Song, Hanjun Dai, Qingbin Li, Le Song

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.

Dimensionality Reduction regression +2

Sequential Information Guided Sensing

no code implementations1 Sep 2015 Ruiyang Song, Yao Xie, Sebastian Pokutta

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.

Scan $B$-Statistic for Kernel Change-Point Detection

no code implementations5 Jul 2015 Shuang Li, Yao Xie, Hanjun Dai, Le Song

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.

Change Point Detection

Categorical Matrix Completion

no code implementations2 Jul 2015 Yang Cao, Yao Xie

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.

Matrix Completion

Sketching for Sequential Change-Point Detection

no code implementations25 May 2015 Yang Cao, Andrew Thompson, Meng Wang, Yao Xie

We study sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics.

Change Point Detection

Poisson Matrix Recovery and Completion

no code implementations20 Apr 2015 Yang Cao, Yao Xie

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.

Matrix Completion

Poisson Matrix Completion

no code implementations26 Jan 2015 Yang Cao, Yao Xie

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.

Matrix Completion

Sequential Sensing with Model Mismatch

no code implementations26 Jan 2015 Ruiyang Song, Yao Xie, Sebastian Pokutta

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

no code implementations22 Jul 2014 David Marangoni-Simonsen, Yao Xie

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.

Online Community Detection

Info-Greedy sequential adaptive compressed sensing

no code implementations2 Jul 2014 Gabor Braun, Sebastian Pokutta, Yao Xie

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.

Fast Algorithm for Low-rank matrix recovery in Poisson noise

no code implementations2 Jul 2014 Yang Cao, Yao Xie

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.

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