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06/14/2023
Integrating Uncertainty Awareness into Conformalized Quantile Regression
Conformalized Quantile Regression (CQR) is a recently proposed method fo...
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06/10/2023
Distribution-free inference with hierarchical data
This paper studies distribution-free inference in settings where the dat...
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06/01/2023
Training neural operators to preserve invariant measures of chaotic attractors
Chaotic systems make long-horizon forecasts difficult because small pert...
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05/31/2023
Deep Stochastic Mechanics
This paper introduces a novel deep-learning-based approach for numerical...
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05/24/2023
Linear Neural Network Layers Promote Learning Single- and Multiple-Index Models
This paper explores the implicit bias of overparameterized neural networ...
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01/30/2023
Bagging Provides Assumption-free Stability
Bagging is an important technique for stabilizing machine learning model...
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01/27/2023
Reduced-Order Autodifferentiable Ensemble Kalman Filters
This paper introduces a computational framework to reconstruct and forec...
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11/29/2022
Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting
Producing high-quality forecasts of key climate variables such as temper...
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11/03/2022
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
This paper explores learning emulators for parameter estimation with unc...
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09/30/2022
Cloud Classification with Unsupervised Deep Learning
We present a framework for cloud characterization that leverages modern ...
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03/16/2022
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction
Datacenters execute large computational jobs, which are composed of smal...
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02/02/2022
The Role of Linear Layers in Nonlinear Interpolating Networks
This paper explores the implicit bias of overparameterized neural networ...
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10/14/2021
Adaptive Differentially Private Empirical Risk Minimization
We propose an adaptive (stochastic) gradient perturbation method for dif...
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07/16/2021
Auto-differentiable Ensemble Kalman Filters
Data assimilation is concerned with sequentially estimating a temporally...
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06/22/2021
Pure Exploration in Kernel and Neural Bandits
We study pure exploration in bandits, where the dimension of the feature...
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03/25/2021
Prediction in the presence of response-dependent missing labels
In a variety of settings, limitations of sensing technologies or other s...
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03/08/2021
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Advanced satellite-born remote sensing instruments produce high-resoluti...
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02/16/2021
Deep Equilibrium Architectures for Inverse Problems in Imaging
Recent efforts on solving inverse problems in imaging via deep neural ne...
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12/01/2020
Functional Linear Regression with Mixed Predictors
We study a functional linear regression model that deals with functional...
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11/30/2020
Model Adaptation for Inverse Problems in Imaging
Deep neural networks have been applied successfully to a wide variety of...
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11/27/2020
Functional Autoregressive Processes in Reproducing Kernel Hilbert Spaces
We study the estimation and prediction of functional autoregressive (FAR...
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10/20/2020
Localizing Changes in High-Dimensional Regression Models
This paper addresses the problem of localizing change points in high-dim...
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06/05/2020
Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes
High-dimensional self-exciting point processes have been widely used in ...
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05/12/2020
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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03/27/2020
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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03/16/2020
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions
High-dimensional autoregressive point processes model how current events...
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02/26/2020
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation
This paper describes a flexible framework for generalized low-rank tenso...
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10/03/2019
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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09/12/2019
Localizing Changes in High-Dimensional Vector Autoregressive Processes
Autoregressive models capture stochastic processes in which past realiza...
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06/26/2019
Statistically and Computationally Efficient Change Point Localization in Regression Settings
Detecting when the underlying distribution changes from the observed tim...
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01/13/2019
Neumann Networks for Inverse Problems in Imaging
Many challenging image processing tasks can be described by an ill-posed...
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01/08/2019
Bilinear Bandits with Low-rank Structure
We introduce the bilinear bandit problem with low-rank structure where a...
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11/07/2018
Estimating Network Structure from Incomplete Event Data
Multivariate Bernoulli autoregressive (BAR) processes model time series ...
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04/26/2018
Tensor Methods for Nonlinear Matrix Completion
In the low rank matrix completion (LRMC) problem, the low rank assumptio...
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03/20/2018
Graph-based regularization for regression problems with highly-correlated designs
Sparse models for high-dimensional linear regression and machine learnin...
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02/26/2018
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes
High-dimensional time series data exist in numerous areas such as financ...
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02/13/2018
Network Estimation from Point Process Data
Consider observing a collection of discrete events within a network that...
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11/06/2017
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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07/08/2017
Subspace Clustering with Missing and Corrupted Data
Subspace clustering is the process of identifying a union of subspaces m...
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06/01/2017
Scalable Generalized Linear Bandits: Online Computation and Hashing
Generalized Linear Bandits (GLBs), a natural extension of the stochastic...
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03/28/2017
Algebraic Variety Models for High-Rank Matrix Completion
We consider a generalization of low-rank matrix completion to the case w...
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10/14/2016
Improved Strongly Adaptive Online Learning using Coin Betting
This paper describes a new parameter-free online learning algorithm for ...
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09/12/2016
Online Data Thinning via Multi-Subspace Tracking
In an era of ubiquitous large-scale streaming data, the availability of ...
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05/09/2016
Inference of High-dimensional Autoregressive Generalized Linear Models
Vector autoregressive models characterize a variety of time series in wh...
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03/13/2016
On Learning High Dimensional Structured Single Index Models
Single Index Models (SIMs) are simple yet flexible semi-parametric model...
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12/29/2015
Matrix Completion Under Monotonic Single Index Models
Most recent results in matrix completion assume that the matrix under co...
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08/31/2012
A two-stage denoising filter: the preprocessed Yaroslavsky filter
This paper describes a simple image noise removal method which combines ...
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08/24/2012
Changepoint detection for high-dimensional time series with missing data
This paper describes a novel approach to change-point detection when the...
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06/02/2012
Poisson noise reduction with non-local PCA
Photon-limited imaging arises when the number of photons collected by a ...
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12/19/2011
