1 code implementation • 22 May 2024 • Jake A. Soloff, Rina Foygel Barber, Rebecca Willett

We propose a new framework for algorithmic stability in the context of multiclass classification.

no code implementations • 21 May 2024 • Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett

Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts.

no code implementations • 16 Apr 2024 • Xiao Zhang, Ruoxi Jiang, William Gao, Rebecca Willett, Michael Maire

We demonstrate that adding a weighting factor to decay the strength of identity shortcuts within residual networks substantially improves semantic feature learning in the state-of-the-art self-supervised masked autoencoding (MAE) paradigm.

no code implementations • 13 Feb 2024 • Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro

We also show that a similar statement in the reverse direction is not possible: any function learnable with polynomial sample complexity by a norm-controlled depth-2 ReLU network with infinite width is also learnable with polynomial sample complexity by a norm-controlled depth-3 ReLU network.

1 code implementation • 27 Jul 2023 • Owen Melia, Eric Jonas, Rebecca Willett

Specifically, we extend the random features method of Rahimi & Recht 2007 by deriving a version that is invariant to three-dimensional rotations and showing that it is fast to evaluate on point cloud data.

1 code implementation • 14 Jun 2023 • Raphael Rossellini, Rina Foygel Barber, Rebecca Willett

The reason is that the prediction intervals of CQR do not distinguish between two forms of uncertainty: first, the variability of the conditional distribution of $Y$ given $X$ (i. e., aleatoric uncertainty), and second, our uncertainty in estimating this conditional distribution (i. e., epistemic uncertainty).

1 code implementation • NeurIPS 2023 • Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett

In this paper, we propose an alternative framework designed to preserve invariant measures of chaotic attractors that characterize the time-invariant statistical properties of the dynamics.

2 code implementations • 31 May 2023 • Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett

This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr\"odinger equation inspired by stochastic mechanics and generative diffusion models.

no code implementations • 24 May 2023 • Suzanna Parkinson, Greg Ongie, Rebecca Willett

This paper explores the implicit bias of overparameterized neural networks of depth greater than two layers.

1 code implementation • 30 Jan 2023 • Jake A. Soloff, Rina Foygel Barber, Rebecca Willett

Bagging is an important technique for stabilizing machine learning models.

1 code implementation • 27 Jan 2023 • Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett

This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical system.

1 code implementation • 29 Nov 2022 • Elena Orlova, Haokun Liu, Raphael Rossellini, Benjamin A. Cash, Rebecca Willett

Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting.

1 code implementation • 3 Nov 2022 • Ruoxi Jiang, Rebecca Willett

This paper explores learning emulators for parameter estimation with uncertainty estimation of high-dimensional dynamical systems.

no code implementations • 30 Sep 2022 • Takuya Kurihana, Ian Foster, Rebecca Willett, Sydney Jenkins, Kathryn Koenig, Ruby Werman, Ricardo Barros Lourenco, Casper Neo, Elisabeth Moyer

We present a framework for cloud characterization that leverages modern unsupervised deep learning technologies.

1 code implementation • 16 Mar 2022 • Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hoffmann

To predict stragglers accurately and early without labeled positive examples or assumptions on latency distributions, this paper presents NURD, a novel Negative-Unlabeled learning approach with Reweighting and Distribution-compensation that only trains on negative and unlabeled streaming data.

no code implementations • 2 Feb 2022 • Greg Ongie, Rebecca Willett

This paper explores the implicit bias of overparameterized neural networks of depth greater than two layers.

no code implementations • 14 Oct 2021 • Xiaoxia Wu, Lingxiao Wang, Irina Cristali, Quanquan Gu, Rebecca Willett

We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization.

1 code implementation • 16 Jul 2021 • Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett

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

no code implementations • NeurIPS 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 misspecification.

no code implementations • 25 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.

no code implementations • 8 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.

1 code implementation • 16 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.

1 code implementation • 1 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

no code implementations • 30 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.

no code implementations • 12 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.

no code implementations • 27 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.

no code implementations • 16 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.

no code implementations • 26 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.

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.

no code implementations • NeurIPS Workshop Deep_Invers 2019 • Greg Ongie, Davis Gilton, Rebecca Willett

Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training data that can outperform more traditional regularized least squares solutions.

2 code implementations • 13 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.

no code implementations • 8 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.

no code implementations • 7 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.

no code implementations • 26 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.

no code implementations • 20 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.

no code implementations • 26 Feb 2018 • Amin Jalali, Rebecca Willett

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

no code implementations • 13 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.

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.

no code implementations • 6 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.

no code implementations • 8 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.

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.

1 code implementation • 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.

no code implementations • 14 Oct 2016 • Kwang-Sung Jun, Francesco Orabona, Rebecca Willett, Stephen Wright

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

no code implementations • 12 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.

no code implementations • 9 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.

no code implementations • 13 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.

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.

no code implementations • 14 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.

no code implementations • 28 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.

no code implementations • 2 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.

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.

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