Search Results for author: Rebecca Willett

Found 33 papers, 3 papers with code

Auto-differentiable Ensemble Kalman Filters

1 code implementation16 Jul 2021 Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett

Data assimilation is concerned with sequentially estimating a temporally-evolving state.

Pure Exploration in Kernel and Neural Bandits

no code implementations22 Jun 2021 Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak

To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space and carefully deal with the induced model misspecifications.

Prediction in the presence of response-dependent missing labels

no code implementations25 Mar 2021 Hyebin Song, Garvesh Raskutti, Rebecca Willett

In a variety of settings, limitations of sensing technologies or other sampling mechanisms result in missing labels, where the likelihood of a missing label in the training set is an unknown function of the data.

Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder

no code implementations8 Mar 2021 Takuya Kurihana, Elisabeth Moyer, Rebecca Willett, Davis Gilton, Ian Foster

Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence.

Deep Equilibrium Architectures for Inverse Problems in Imaging

1 code implementation16 Feb 2021 Davis Gilton, Gregory Ongie, Rebecca Willett

Recent efforts on solving inverse problems in imaging via deep neural networks use architectures inspired by a fixed number of iterations of an optimization method.

Functional Linear Regression with Mixed Predictors

no code implementations1 Dec 2020 Daren Wang, Zifeng Zhao, Yi Yu, Rebecca Willett

We derive finite sample theoretical guarantees and show that the excess prediction risk of our estimator is minimax optimal.

Statistics Theory Methodology Statistics Theory

Model Adaptation for Inverse Problems in Imaging

no code implementations30 Nov 2020 Davis Gilton, Gregory Ongie, Rebecca Willett

Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging.

Deblurring Image Reconstruction +1

Deep Learning Techniques for Inverse Problems in Imaging

no code implementations12 May 2020 Gregory Ongie, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, Rebecca Willett

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging.

Detection and Description of Change in Visual Streams

no code implementations27 Mar 2020 Davis Gilton, Ruotian Luo, Rebecca Willett, Greg Shakhnarovich

This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps.

Representation Learning

Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions

no code implementations16 Mar 2020 Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark

High-dimensional autoregressive point processes model how current events trigger or inhibit future events, such as activity by one member of a social network can affect the future activity of his or her neighbors.

Point Processes Time Series

An Optimal Statistical and Computational Framework for Generalized Tensor Estimation

no code implementations26 Feb 2020 Rungang Han, Rebecca Willett, Anru R. Zhang

Under mild conditions on the loss function, we establish both an upper bound on statistical error and the linear rate of computational convergence through a general deterministic analysis.

Denoising

A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case

no code implementations ICLR 2020 Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro

In this paper, we characterize the norm required to realize a function $f:\mathbb{R}^d\rightarrow\mathbb{R}$ as a single hidden-layer ReLU network with an unbounded number of units (infinite width), but where the Euclidean norm of the weights is bounded, including precisely characterizing which functions can be realized with finite norm.

Neumann Networks for Inverse Problems in Imaging

2 code implementations13 Jan 2019 Davis Gilton, Greg Ongie, Rebecca Willett

We present an end-to-end, data-driven method of solving inverse problems inspired by the Neumann series, which we call a Neumann network.

Deblurring

Bilinear Bandits with Low-rank Structure

no code implementations8 Jan 2019 Kwang-Sung Jun, Rebecca Willett, Stephen Wright, Robert Nowak

We introduce the bilinear bandit problem with low-rank structure in which an action takes the form of a pair of arms from two different entity types, and the reward is a bilinear function of the known feature vectors of the arms.

Estimating Network Structure from Incomplete Event Data

no code implementations7 Nov 2018 Benjamin Mark, Garvesh Raskutti, Rebecca Willett

Multivariate Bernoulli autoregressive (BAR) processes model time series of events in which the likelihood of current events is determined by the times and locations of past events.

Time Series

Tensor Methods for Nonlinear Matrix Completion

no code implementations26 Apr 2018 Greg Ongie, Daniel Pimentel-Alarcón, Laura Balzano, Rebecca Willett, Robert D. Nowak

This approach will succeed in many cases where traditional LRMC is guaranteed to fail because the data are low-rank in the tensorized representation but not in the original representation.

Low-Rank Matrix Completion

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

no code implementations20 Mar 2018 Yuan Li, Benjamin Mark, Garvesh Raskutti, Rebecca Willett, Hyebin Song, David Neiman

This work considers a high-dimensional regression setting in which a graph governs both correlations among the covariates and the similarity among regression coefficients -- meaning there is \emph{alignment} between the covariates and regression coefficients.

Model Selection

Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes

no code implementations26 Feb 2018 Amin Jalali, Rebecca Willett

In such a scenario, where covariates are highly interdependent and partially missing, new theoretical challenges arise.

Time Series

Network Estimation from Point Process Data

no code implementations13 Feb 2018 Benjamin Mark, Garvesh Raskutti, Rebecca Willett

Using our general framework, we provide a number of novel theoretical guarantees for high-dimensional self-exciting point processes that reflect the role played by the underlying network structure and long-term memory.

Point Processes

Subspace Clustering via Tangent Cones

no code implementations NeurIPS 2017 Amin Jalali, Rebecca Willett

Given samples lying on any of a number of subspaces, subspace clustering is the task of grouping the samples based on the their corresponding subspaces.

Graph Clustering

Online Learning for Changing Environments using Coin Betting

no code implementations6 Nov 2017 Kwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett

A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments.

Metric Learning

Subspace Clustering with Missing and Corrupted Data

no code implementations8 Jul 2017 Zachary Charles, Amin Jalali, Rebecca Willett

Given full or partial information about a collection of points that lie close to a union of several subspaces, subspace clustering refers to the process of clustering the points according to their subspace and identifying the subspaces.

Scalable Generalized Linear Bandits: Online Computation and Hashing

no code implementations NeurIPS 2017 Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, Rebecca Willett

Second, for the case where the number $N$ of arms is very large, we propose new algorithms in which each next arm is selected via an inner product search.

Algebraic Variety Models for High-Rank Matrix Completion

no code implementations ICML 2017 Greg Ongie, Rebecca Willett, Robert D. Nowak, Laura Balzano

We consider a generalization of low-rank matrix completion to the case where the data belongs to an algebraic variety, i. e. each data point is a solution to a system of polynomial equations.

Low-Rank Matrix Completion

Improved Strongly Adaptive Online Learning using Coin Betting

no code implementations14 Oct 2016 Kwang-Sung Jun, Francesco Orabona, Rebecca Willett, Stephen Wright

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

Metric Learning

Online Data Thinning via Multi-Subspace Tracking

no code implementations12 Sep 2016 Xin Jiang, Rebecca Willett

At the heart of this proposed approach is an online anomaly detection method based on dynamic, low-rank Gaussian mixture models.

Anomaly Detection

Inference of High-dimensional Autoregressive Generalized Linear Models

no code implementations9 May 2016 Eric C. Hall, Garvesh Raskutti, Rebecca Willett

For instance, each element of an observation vector could correspond to a different node in a network, and the parameters of an autoregressive model would correspond to the impact of the network structure on the time series evolution.

Time Series

On Learning High Dimensional Structured Single Index Models

no code implementations13 Mar 2016 Nikhil Rao, Ravi Ganti, Laura Balzano, Rebecca Willett, Robert Nowak

Single Index Models (SIMs) are simple yet flexible semi-parametric models for machine learning, where the response variable is modeled as a monotonic function of a linear combination of features.

Matrix Completion Under Monotonic Single Index Models

no code implementations NeurIPS 2015 Ravi Ganti, Laura Balzano, Rebecca Willett

Most recent results in matrix completion assume that the matrix under consideration is low-rank or that the columns are in a union of low-rank subspaces.

Matrix Completion

Online Markov decision processes with Kullback-Leibler control cost

no code implementations14 Jan 2014 Peng Guan, Maxim Raginsky, Rebecca Willett

This paper considers an online (real-time) control problem that involves an agent performing a discrete-time random walk over a finite state space.

Relax but stay in control: from value to algorithms for online Markov decision processes

no code implementations28 Oct 2013 Peng Guan, Maxim Raginsky, Rebecca Willett

Online learning algorithms are designed to perform in non-stationary environments, but generally there is no notion of a dynamic state to model constraints on current and future actions as a function of past actions.

Poisson noise reduction with non-local PCA

no code implementations2 Jun 2012 Joseph Salmon, Zachary Harmany, Charles-Alban Deledalle, Rebecca Willett

Photon-limited imaging arises when the number of photons collected by a sensor array is small relative to the number of detector elements.

Denoising Dictionary Learning

Near-minimax recursive density estimation on the binary hypercube

no code implementations NeurIPS 2008 Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett, Jorge Silva

This paper describes a recursive estimation procedure for multivariate binary densities using orthogonal expansions.

Density Estimation

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