Rebecca Willett

research

∙ 06/01/2023

Training neural operators to preserve invariant measures of chaotic attractors

Chaotic systems make long-horizon forecasts difficult because small pert...

0 Ruoxi Jiang, et al. ∙

research

∙ 05/31/2023

Deep Stochastic Mechanics

This paper introduces a novel deep-learning-based approach for numerical...

0 Elena Orlova, et al. ∙

research

∙ 05/24/2023

Linear Neural Network Layers Promote Learning Single- and Multiple-Index Models

This paper explores the implicit bias of overparameterized neural networ...

0 Suzanna Parkinson, et al. ∙

research

∙ 01/30/2023

Bagging Provides Assumption-free Stability

Bagging is an important technique for stabilizing machine learning model...

0 Jake A. Soloff, et al. ∙

research

∙ 01/27/2023

Reduced-Order Autodifferentiable Ensemble Kalman Filters

This paper introduces a computational framework to reconstruct and forec...

0 Yuming Chen, et al. ∙

research

∙ 11/29/2022

Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting

Producing high-quality forecasts of key climate variables such as temper...

0 Elena Orlova, et al. ∙

research

∙ 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...

0 Ruoxi Jiang, et al. ∙

research

∙ 09/30/2022

Cloud Classification with Unsupervised Deep Learning

We present a framework for cloud characterization that leverages modern ...

0 Takuya Kurihana, et al. ∙

research

∙ 03/16/2022

NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction

Datacenters execute large computational jobs, which are composed of smal...

0 Yi Ding, et al. ∙

research

∙ 02/02/2022

The Role of Linear Layers in Nonlinear Interpolating Networks

This paper explores the implicit bias of overparameterized neural networ...

0 Greg Ongie, et al. ∙

research

∙ 10/14/2021

Adaptive Differentially Private Empirical Risk Minimization

We propose an adaptive (stochastic) gradient perturbation method for dif...

12 Xiaoxia Wu, et al. ∙

research

∙ 07/16/2021

Auto-differentiable Ensemble Kalman Filters

Data assimilation is concerned with sequentially estimating a temporally...

0 Yuming Chen, et al. ∙

research

∙ 06/22/2021

Pure Exploration in Kernel and Neural Bandits

We study pure exploration in bandits, where the dimension of the feature...

20 Yinglun Zhu, et al. ∙

research

∙ 03/25/2021

Prediction in the presence of response-dependent missing labels

In a variety of settings, limitations of sensing technologies or other s...

0 Hyebin Song, et al. ∙

research

∙ 03/08/2021

Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder

Advanced satellite-born remote sensing instruments produce high-resoluti...

12 Takuya Kurihana, et al. ∙

research

∙ 02/16/2021

Deep Equilibrium Architectures for Inverse Problems in Imaging

Recent efforts on solving inverse problems in imaging via deep neural ne...

0 Davis Gilton, et al. ∙

research

∙ 12/01/2020

Functional Linear Regression with Mixed Predictors

We study a functional linear regression model that deals with functional...

0 Daren Wang, et al. ∙

research

∙ 11/30/2020

Model Adaptation for Inverse Problems in Imaging

Deep neural networks have been applied successfully to a wide variety of...

0 Davis Gilton, et al. ∙

research

∙ 11/27/2020

Functional Autoregressive Processes in Reproducing Kernel Hilbert Spaces

We study the estimation and prediction of functional autoregressive (FAR...

0 Daren Wang, et al. ∙

research

∙ 10/20/2020

Localizing Changes in High-Dimensional Regression Models

This paper addresses the problem of localizing change points in high-dim...

0 Alessandro Rinaldo, et al. ∙

research

∙ 06/05/2020

Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes

High-dimensional self-exciting point processes have been widely used in ...

0 Daren Wang, et al. ∙

research

∙ 05/12/2020

Deep Learning Techniques for Inverse Problems in Imaging

Recent work in machine learning shows that deep neural networks can be u...

11 Gregory Ongie, et al. ∙

research

∙ 03/27/2020

Detection and Description of Change in Visual Streams

This paper presents a framework for the analysis of changes in visual st...

0 Davis Gilton, et al. ∙

research

∙ 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...

0 Lili Zheng, et al. ∙

research

∙ 02/26/2020

An Optimal Statistical and Computational Framework for Generalized Tensor Estimation

This paper describes a flexible framework for generalized low-rank tenso...

0 Rungang Han, et al. ∙

research

∙ 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 ...

0 Greg Ongie, et al. ∙

research

∙ 09/12/2019

Localizing Changes in High-Dimensional Vector Autoregressive Processes

Autoregressive models capture stochastic processes in which past realiza...

0 Daren Wang, et al. ∙

research

∙ 06/26/2019

Statistically and Computationally Efficient Change Point Localization in Regression Settings

Detecting when the underlying distribution changes from the observed tim...

0 Daren Wang, et al. ∙

research

∙ 01/13/2019

Neumann Networks for Inverse Problems in Imaging

Many challenging image processing tasks can be described by an ill-posed...

0 Davis Gilton, et al. ∙

research

∙ 01/08/2019

Bilinear Bandits with Low-rank Structure

We introduce the bilinear bandit problem with low-rank structure where a...

0 Kwang-Sung Jun, et al. ∙

research

∙ 11/07/2018

Estimating Network Structure from Incomplete Event Data

Multivariate Bernoulli autoregressive (BAR) processes model time series ...

0 Benjamin Mark, et al. ∙

research

∙ 04/26/2018

Tensor Methods for Nonlinear Matrix Completion

In the low rank matrix completion (LRMC) problem, the low rank assumptio...

0 Greg Ongie, et al. ∙

research

∙ 03/20/2018

Graph-based regularization for regression problems with highly-correlated designs

Sparse models for high-dimensional linear regression and machine learnin...

0 Yuan Li, et al. ∙

research

∙ 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...

0 Amin Jalali, et al. ∙

research

∙ 02/13/2018

Network Estimation from Point Process Data

Consider observing a collection of discrete events within a network that...

0 Benjamin Mark, et al. ∙

research

∙ 11/06/2017

Online Learning for Changing Environments using Coin Betting

A key challenge in online learning is that classical algorithms can be s...

0 Kwang-Sung Jun, et al. ∙

research

∙ 07/08/2017

Subspace Clustering with Missing and Corrupted Data

Subspace clustering is the process of identifying a union of subspaces m...

0 Zachary Charles, et al. ∙

research

∙ 06/01/2017

Scalable Generalized Linear Bandits: Online Computation and Hashing

Generalized Linear Bandits (GLBs), a natural extension of the stochastic...

0 Kwang-Sung Jun, et al. ∙

research

∙ 03/28/2017

Algebraic Variety Models for High-Rank Matrix Completion

We consider a generalization of low-rank matrix completion to the case w...

0 Greg Ongie, et al. ∙

research

∙ 10/14/2016

Improved Strongly Adaptive Online Learning using Coin Betting

This paper describes a new parameter-free online learning algorithm for ...

0 Kwang-Sung Jun, et al. ∙

research

∙ 09/12/2016

Online Data Thinning via Multi-Subspace Tracking

In an era of ubiquitous large-scale streaming data, the availability of ...

0 Xin Jiang, et al. ∙

research

∙ 05/09/2016

Inference of High-dimensional Autoregressive Generalized Linear Models

Vector autoregressive models characterize a variety of time series in wh...

0 Eric C. Hall, et al. ∙

research

∙ 03/13/2016

On Learning High Dimensional Structured Single Index Models

Single Index Models (SIMs) are simple yet flexible semi-parametric model...

0 Nikhil Rao, et al. ∙

research

∙ 12/29/2015

Matrix Completion Under Monotonic Single Index Models

Most recent results in matrix completion assume that the matrix under co...

0 Ravi Ganti, et al. ∙

research

∙ 08/31/2012

A two-stage denoising filter: the preprocessed Yaroslavsky filter

This paper describes a simple image noise removal method which combines ...

0 Joseph Salmon, et al. ∙

research

∙ 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...

0 Yao Xie, et al. ∙

research

∙ 06/02/2012

Poisson noise reduction with non-local PCA

Photon-limited imaging arises when the number of photons collected by a ...

0 Joseph Salmon, et al. ∙

research

∙ 12/19/2011

Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods

This paper describes a novel theoretical characterization of the perform...

0 Ery Arias-Castro, et al. ∙

research

∙ 12/07/2011

A recursive procedure for density estimation on the binary hypercube

This paper describes a recursive estimation procedure for multivariate b...

0 Maxim Raginsky, et al. ∙

Rebecca Willett

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