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.

no code implementations • 11 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.

1 code implementation • 8 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.

no code implementations • 4 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.

no code implementations • 27 May 2024 • JongHyeok Lee, Chen Xu, Yao Xie

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

no code implementations • 24 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.

no code implementations • 21 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.

1 code implementation • 6 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.

no code implementations • 5 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.

no code implementations • 4 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.

1 code implementation • 30 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.

no code implementations • 30 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.

1 code implementation • 26 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.

no code implementations • 5 Oct 2023 • Song Wei, Xiangrui Kong, Alinson Santos Xavier, Shixiang Zhu, Yao Xie, Feng Qiu

Energy justice is a growing area of interest in interdisciplinary energy research.

no code implementations • 10 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.

no code implementations • 20 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.

no code implementations • 19 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.

no code implementations • 16 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.

no code implementations • 26 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.

no code implementations • 15 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.

no code implementations • 26 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.

no code implementations • 26 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.

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.

1 code implementation • 7 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}).

no code implementations • 29 Nov 2022 • Khurram Yamin, Nima Jadali, Dima Nazzal, Yao Xie

We generate a clean election results dataset without fraud as well as datasets with varying degrees of fraud.

1 code implementation • 28 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.

no code implementations • 22 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.

no code implementations • 21 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.

no code implementations • 31 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.

1 code implementation • 9 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.

no code implementations • 11 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.

1 code implementation • 7 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.

2 code implementations • 15 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.

1 code implementation • 2 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.

no code implementations • 4 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.

no code implementations • 8 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.

1 code implementation • 17 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).

no code implementations • 9 Feb 2022 • Jie Wang, Yao Xie

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

no code implementations • 8 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.

no code implementations • 8 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.

1 code implementation • 24 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.

no code implementations • 8 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.

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.

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.

no code implementations • 4 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.

no code implementations • 31 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.

1 code implementation • 25 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.

1 code implementation • 7 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 $\gamma$.

no code implementations • 18 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.

no code implementations • 30 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\%.

no code implementations • 11 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

no code implementations • 10 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.

no code implementations • 9 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

no code implementations • 27 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.

no code implementations • 26 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

no code implementations • 5 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

no code implementations • 24 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.

no code implementations • 22 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.

2 code implementations • 18 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.

no code implementations • 16 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.

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.

no code implementations • 7 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.

no code implementations • 15 May 2020 • Shixiang Zhu, Ruyi Ding, Minghe Zhang, Pascal Van Hentenryck, Yao Xie

We present a novel framework for modeling traffic congestion events over road networks.

no code implementations • 29 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.

no code implementations • 17 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.

no code implementations • 21 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.

no code implementations • 20 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.

no code implementations • 11 Sep 2019 • Alexander Shapiro, Yao Xie, Rui Zhang

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

1 code implementation • 13 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.

no code implementations • 11 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.

no code implementations • 1 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.

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.

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.

no code implementations • 15 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.

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.

no code implementations • 11 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.

no code implementations • 31 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.

no code implementations • 28 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).

no code implementations • 15 Jun 2017 • Liyan Xie, Yao Xie

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

no code implementations • 19 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.

no code implementations • 5 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.

no code implementations • 14 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.

no code implementations • 3 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.

no code implementations • 29 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.

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.

no code implementations • 1 Sep 2015 • Yang Cao, Yao Xie, Nagi Gebraeel

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

no code implementations • 1 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.

no code implementations • 1 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.

no code implementations • 5 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.

no code implementations • 2 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.

no code implementations • 25 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.

no code implementations • 20 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.

no code implementations • 26 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.

no code implementations • 26 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.

no code implementations • 22 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.

no code implementations • 2 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.

no code implementations • 2 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.

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