Rachel A. Ward

According to our database, Rachel A. Ward authored at least 84 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Generating synthetic data for neural operators.

Erisa Hasani, Rachel A. Ward

CoRR, 2024

2023

Learning to Forecast Dynamical Systems from Streaming Data.

Dimitrios Giannakis, Amelia Henriksen, Joel A. Tropp, Rachel A. Ward

SIAM J. Appl. Dyn. Syst., June, 2023

Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering.

Yijun Dong, Kevin Miller, Qi Lei, Rachel A. Ward

CoRR, 2023

Robust Implicit Regularization via Weight Normalization.

Hung-Hsu Chou, Holger Rauhut, Rachel A. Ward

CoRR, 2023

Convergence of Alternating Gradient Descent for Matrix Factorization.

Rachel A. Ward, Tamara G. Kolda

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Nearly Optimal Bounds for Cyclic Forgetting.

William Swartworth, Deanna Needell, Rachel A. Ward, Mark Kong, Halyun Jeong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift.

Yijun Dong, Yuege Xie, Rachel A. Ward

Proceedings of the International Conference on Machine Learning, 2023

Sample Efficiency of Data Augmentation Consistency Regularization.

Shuo Yang, Yijun Dong, Rachel A. Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Johnson-Lindenstrauss Embeddings with Kronecker Structure.

Stefan Bamberger, Felix Krahmer, Rachel A. Ward

SIAM J. Matrix Anal. Appl., December, 2022

Overparameterization and Generalization Error: Weighted Trigonometric Interpolation.

Yuege Xie, Hung-Hsu Chou, Holger Rauhut, Rachel A. Ward

SIAM J. Math. Data Sci., June, 2022

Compressed sensing with a jackknife and a bootstrap.

Aaron Defazio, Mark Tygert, Rachel A. Ward, Jure Zbontar

J. Data Sci. Stat. Vis., 2022

Matrix Concentration for Products.

De Huang, Jonathan Niles-Weed, Joel A. Tropp, Rachel A. Ward

Found. Comput. Math., 2022

AdaWAC: Adaptively Weighted Augmentation Consistency Regularization for Volumetric Medical Image Segmentation.

Yijun Dong, Yuege Xie, Rachel A. Ward

CoRR, 2022

On the fast convergence of minibatch heavy ball momentum.

Raghu Bollapragada, Tyler Chen, Rachel A. Ward

CoRR, 2022

An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models.

Nhat Ho, Tongzheng Ren, Sujay Sanghavi, Purnamrita Sarkar, Rachel A. Ward

CoRR, 2022

Scalable symmetric Tucker tensor decomposition.

Ruhui Jin, Joe Kileel, Tamara G. Kolda, Rachel A. Ward

CoRR, 2022

Side-effects of Learning from Low Dimensional Data Embedded in an Euclidean Space.

Juncai He, Richard Tsai, Rachel A. Ward

CoRR, 2022

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning.

Yuege Xie, Robert Shi, Hayden Schaeffer, Rachel A. Ward

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Concentration of Random Feature Matrices in High-Dimensions.

Zhijun Chen, Hayden Schaeffer, Rachel A. Ward

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Arbitrary-Length Analogs to de Bruijn Sequences.

Abhinav Nellore, Rachel A. Ward

Proceedings of the 33rd Annual Symposium on Combinatorial Pattern Matching, 2022

The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance.

Matthew Faw, Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari, Sanjay Shakkottai, Rachel A. Ward

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

How catastrophic can catastrophic forgetting be in linear regression?

Itay Evron, Edward Moroshko, Rachel A. Ward, Nathan Srebro, Daniel Soudry

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method.

Xiaoxia Wu, Yuege Xie, Simon Shaolei Du, Rachel A. Ward

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning.

Yuege Xie, Bobby Shi, Hayden Schaeffer, Rachel A. Ward

CoRR, 2021

Learning to Forecast Dynamical Systems from Streaming Data.

Dimitris Giannakis, Amelia Henriksen, Joel A. Tropp, Rachel A. Ward

CoRR, 2021

Function Approximation via Sparse Random Features.

Abolfazl Hashemi, Hayden Schaeffer, Robert Shi, Ufuk Topcu, Giang Tran, Rachel A. Ward

CoRR, 2021

Bootstrapping the Error of Oja's Algorithm.

Robert Lunde, Purnamrita Sarkar, Rachel A. Ward

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates.

De Huang, Jonathan Niles-Weed, Rachel A. Ward

Proceedings of the Conference on Learning Theory, 2021

2020

Extracting Structured Dynamical Systems Using Sparse Optimization With Very Few Samples.

Hayden Schaeffer, Giang Tran, Rachel A. Ward, Linan Zhang

Multiscale Model. Simul., 2020

Recovery guarantees for polynomial coefficients from weakly dependent data with outliers.

Lam Si Tung Ho, Hayden Schaeffer, Giang Tran, Rachel A. Ward

J. Approx. Theory, 2020

Weighted Optimization: better generalization by smoother interpolation.

Yuege Xie, Rachel A. Ward, Holger Rauhut, Hung-Hsu Chou

CoRR, 2020

Implicit Regularization and Convergence for Weight Normalization.

Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Linear Convergence of Adaptive Stochastic Gradient Descent.

Yuege Xie, Xiaoxia Wu, Rachel A. Ward

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Implicit Regularization of Normalization Methods.

Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu

CoRR, 2019

Faster Johnson-Lindenstrauss Transforms via Kronecker Products.

Ruhui Jin, Tamara G. Kolda, Rachel A. Ward

CoRR, 2019

Bias of Homotopic Gradient Descent for the Hinge Loss.

Denali Molitor, Deanna Needell, Rachel A. Ward

CoRR, 2019

AdaOja: Adaptive Learning Rates for Streaming PCA.

Amelia Henriksen, Rachel A. Ward

CoRR, 2019

Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network.

Xiaoxia Wu, Simon S. Du, Rachel A. Ward

CoRR, 2019

AdaGrad stepsizes: sharp convergence over nonconvex landscapes.

Rachel A. Ward, Xiaoxia Wu, Léon Bottou

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Extracting Sparse High-Dimensional Dynamics from Limited Data.

Hayden Schaeffer, Giang Tran, Rachel A. Ward

SIAM J. Appl. Math., 2018

Recovery guarantees for polynomial approximation from dependent data with outliers.

Lam Si Tung Ho, Hayden Schaeffer, Giang Tran, Rachel A. Ward

CoRR, 2018

AdaGrad stepsizes: Sharp convergence over nonconvex landscapes, from any initialization.

Rachel A. Ward, Xiaoxia Wu, Léon Bottou

CoRR, 2018

Greedy Variance Estimation for the LASSO.

Christopher Kennedy, Rachel A. Ward

CoRR, 2018

WNGrad: Learn the Learning Rate in Gradient Descent.

Xiaoxia Wu, Rachel A. Ward, Léon Bottou

CoRR, 2018

2017

Exact Recovery of Chaotic Systems from Highly Corrupted Data.

Giang Tran, Rachel A. Ward

Multiscale Model. Simul., 2017

Fast Cross-Polytope Locality-Sensitive Hashing.

Christopher Kennedy, Rachel A. Ward

Proceedings of the 8th Innovations in Theoretical Computer Science Conference, 2017

Learning the second-moment matrix of a smooth function from point samples.

Armin Eftekhari, Michael B. Wakin, Ping Li, Paul G. Constantine, Rachel A. Ward

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016

The Sample Complexity of Weighted Sparse Approximation.

Bubacarr Bah, Rachel A. Ward

IEEE Trans. Signal Process., 2016

One-Bit Compressive Sensing With Norm Estimation.

Karin Knudson, Rayan Saab, Rachel A. Ward

IEEE Trans. Inf. Theory, 2016

Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm.

Deanna Needell, Nathan Srebro, Rachel A. Ward

Math. Program., 2016

A polynomial-time relaxation of the Gromov-Hausdorff distance.

Soledad Villar, Afonso S. Bandeira, Andrew J. Blumberg, Rachel A. Ward

CoRR, 2016

Batched Stochastic Gradient Descent with Weighted Sampling.

Deanna Needell, Rachel A. Ward

CoRR, 2016

Clustering subgaussian mixtures by semidefinite programming.

Dustin G. Mixon, Soledad Villar, Rachel A. Ward

CoRR, 2016

Improved bounds for sparse recovery from subsampled random convolutions.

Shahar Mendelson, Holger Rauhut, Rachel A. Ward

CoRR, 2016

MC^2: A Two-Phase Algorithm for Leveraged Matrix Completion.

Armin Eftekhari, Michael B. Wakin, Rachel A. Ward

CoRR, 2016

Learning the Differential Correlation Matrix of a Smooth Function From Point Samples.

Armin Eftekhari, Ping Li, Michael B. Wakin, Rachel A. Ward

CoRR, 2016

Clustering subgaussian mixtures with k-means.

Dustin G. Mixon, Soledad Villar, Rachel A. Ward

Proceedings of the 2016 IEEE Information Theory Workshop, 2016

2015

Compressive Sensing with Redundant Dictionaries and Structured Measurements.

Felix Krahmer, Deanna Needell, Rachel A. Ward

SIAM J. Math. Anal., 2015

Completing any low-rank matrix, provably.

Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel A. Ward

J. Mach. Learn. Res., 2015

Recovery guarantees for exemplar-based clustering.

Abhinav Nellore, Rachel A. Ward

Inf. Comput., 2015

Relax, No Need to Round: Integrality of Clustering Formulations.

Pranjal Awasthi, Afonso S. Bandeira, Moses Charikar, Ravishankar Krishnaswamy, Soledad Villar, Rachel A. Ward

Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015

2014

Stable and Robust Sampling Strategies for Compressive Imaging.

Felix Krahmer, Rachel A. Ward

IEEE Trans. Image Process., 2014

A unified framework for linear dimensionality reduction in l₁.

Rachel A. Ward

CoRR, 2014

Coherent Matrix Completion.

Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel A. Ward

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Near-Optimal Compressed Sensing Guarantees for Total Variation Minimization.

Deanna Needell, Rachel A. Ward

IEEE Trans. Image Process., 2013

Stable Image Reconstruction Using Total Variation Minimization.

Deanna Needell, Rachel A. Ward

SIAM J. Imaging Sci., 2013

A Symbol-Based Algorithm for Decoding Bar Codes.

Mark A. Iwen, Fadil Santosa, Rachel A. Ward

SIAM J. Imaging Sci., 2013

Stochastic gradient descent and the randomized Kaczmarz algorithm.

Deanna Needell, Nathan Srebro, Rachel A. Ward

CoRR, 2013

2012

Root-Exponential Accuracy for Coarse Quantization of Finite Frame Expansions.

Felix Krahmer, Rayan Saab, Rachel A. Ward

IEEE Trans. Inf. Theory, 2012

Weighted Eigenfunction Estimates with Applications to Compressed Sensing.

Nicolas Burq, Semyon Dyatlov, Rachel A. Ward, Maciej Zworski

SIAM J. Math. Anal., 2012

Sparse Legendre expansions via l₁-minimization.

Holger Rauhut, Rachel A. Ward

J. Approx. Theory, 2012

Total variation minimization for stable multidimensional signal recovery

Deanna Needell, Rachel A. Ward

CoRR, 2012

Compressive imaging: stable and robust recovery from variable density frequency samples

Felix Krahmer, Rachel A. Ward

CoRR, 2012

2011

New and Improved Johnson-Lindenstrauss Embeddings via the Restricted Isometry Property.

Felix Krahmer, Rachel A. Ward

SIAM J. Math. Anal., 2011

Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization.

Massimo Fornasier, Holger Rauhut, Rachel A. Ward

SIAM J. Optim., 2011

Computing the confidence levels for a root-mean-square test of goodness-of-fit.

William Perkins, Mark Tygert, Rachel A. Ward

Appl. Math. Comput., 2011

Trust in Human-Computer Interactions as Measured by Frustration, Surprise, and Workload.

Leanne M. Hirshfield, Stuart H. Hirshfield, Samuel W. Hincks, Matthew Russell, Rachel A. Ward, Tom Williams

Proceedings of the Foundations of Augmented Cognition. Directing the Future of Adaptive Systems, 2011

This is your brain on interfaces: enhancing usability testing with functional near-infrared spectroscopy.

Leanne M. Hirshfield, Rebecca Gulotta, Stuart H. Hirshfield, Samuel W. Hincks, Matthew Russell, Rachel A. Ward, Tom Williams, Robert J. K. Jacob

Proceedings of the International Conference on Human Factors in Computing Systems, 2011

2010

On the Complexity of Mumford-Shah-Type Regularization, Viewed as a Relaxed Sparsity Constraint.

Boris Alexeev, Rachel A. Ward

IEEE Trans. Image Process., 2010

Iterative Thresholding Meets Free-Discontinuity Problems.

Massimo Fornasier, Rachel A. Ward

Found. Comput. Math., 2010

Lower bounds for the error decay incurred by coarse quantization schemes

Felix Krahmer, Rachel A. Ward

CoRR, 2010

Efficient and stable recovery of Legendre-sparse polynomials.

Holger Rauhut, Rachel A. Ward

Proceedings of the 44th Annual Conference on Information Sciences and Systems, 2010

2009

Compressed sensing with cross validation.

Rachel A. Ward

IEEE Trans. Inf. Theory, 2009

Shape deformation in continuous map generalization.

Jeff Danciger, Satyan L. Devadoss, John Mugno, Don Sheehy, Rachel A. Ward

GeoInformatica, 2009