
Prediction in the presence of responsedependent missing labels
In a variety of settings, limitations of sensing technologies or other s...
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Datadriven Cloud Clustering via a Rotationally Invariant Autoencoder
Advanced satelliteborn remote sensing instruments produce highresoluti...
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Deep Equilibrium Architectures for Inverse Problems in Imaging
Recent efforts on solving inverse problems in imaging via deep neural ne...
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Functional Linear Regression with Mixed Predictors
We study a functional linear regression model that deals with functional...
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Model Adaptation for Inverse Problems in Imaging
Deep neural networks have been applied successfully to a wide variety of...
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Functional Autoregressive Processes in Reproducing Kernel Hilbert Spaces
We study the estimation and prediction of functional autoregressive (FAR...
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Localizing Changes in HighDimensional Regression Models
This paper addresses the problem of localizing change points in highdim...
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Detecting Abrupt Changes in HighDimensional SelfExciting Poisson Processes
Highdimensional selfexciting point processes have been widely used in ...
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Deep Learning Techniques for Inverse Problems in Imaging
Recent work in machine learning shows that deep neural networks can be u...
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Detection and Description of Change in Visual Streams
This paper presents a framework for the analysis of changes in visual st...
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Contextdependent selfexciting point processes: models, methods, and risk bounds in high dimensions
Highdimensional autoregressive point processes model how current events...
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An Optimal Statistical and Computational Framework for Generalized Tensor Estimation
This paper describes a flexible framework for generalized lowrank tenso...
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A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
A key element of understanding the efficacy of overparameterized neural ...
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Localizing Changes in HighDimensional Vector Autoregressive Processes
Autoregressive models capture stochastic processes in which past realiza...
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Statistically and Computationally Efficient Change Point Localization in Regression Settings
Detecting when the underlying distribution changes from the observed tim...
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Neumann Networks for Inverse Problems in Imaging
Many challenging image processing tasks can be described by an illposed...
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Bilinear Bandits with Lowrank Structure
We introduce the bilinear bandit problem with lowrank structure where a...
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Estimating Network Structure from Incomplete Event Data
Multivariate Bernoulli autoregressive (BAR) processes model time series ...
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Tensor Methods for Nonlinear Matrix Completion
In the low rank matrix completion (LRMC) problem, the low rank assumptio...
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Graphbased regularization for regression problems with highlycorrelated designs
Sparse models for highdimensional linear regression and machine learnin...
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Missing Data in Sparse Transition Matrix Estimation for SubGaussian Vector Autoregressive Processes
Highdimensional time series data exist in numerous areas such as financ...
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Network Estimation from Point Process Data
Consider observing a collection of discrete events within a network that...
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Online Learning for Changing Environments using Coin Betting
A key challenge in online learning is that classical algorithms can be s...
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Subspace Clustering with Missing and Corrupted Data
Subspace clustering is the process of identifying a union of subspaces m...
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Scalable Generalized Linear Bandits: Online Computation and Hashing
Generalized Linear Bandits (GLBs), a natural extension of the stochastic...
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Algebraic Variety Models for HighRank Matrix Completion
We consider a generalization of lowrank matrix completion to the case w...
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Improved Strongly Adaptive Online Learning using Coin Betting
This paper describes a new parameterfree online learning algorithm for ...
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Online Data Thinning via MultiSubspace Tracking
In an era of ubiquitous largescale streaming data, the availability of ...
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Inference of Highdimensional Autoregressive Generalized Linear Models
Vector autoregressive models characterize a variety of time series in wh...
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On Learning High Dimensional Structured Single Index Models
Single Index Models (SIMs) are simple yet flexible semiparametric model...
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Matrix Completion Under Monotonic Single Index Models
Most recent results in matrix completion assume that the matrix under co...
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A twostage denoising filter: the preprocessed Yaroslavsky filter
This paper describes a simple image noise removal method which combines ...
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Changepoint detection for highdimensional time series with missing data
This paper describes a novel approach to changepoint detection when the...
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Poisson noise reduction with nonlocal PCA
Photonlimited imaging arises when the number of photons collected by a ...
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Oracle inequalities and minimax rates for nonlocal means and related adaptive kernelbased methods
This paper describes a novel theoretical characterization of the perform...
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A recursive procedure for density estimation on the binary hypercube
This paper describes a recursive estimation procedure for multivariate b...
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