Shape deformation in continuous map generalization. - Jeff Danciger; Satyan L. Devadoss; John Mugno; Don Sheehy; Rachel A. Ward (GeoInformatica, 2009)

Compressed sensing with cross validation. - Rachel A. Ward (IEEE Trans. Inf. Theory, 2009)

Efficient and stable recovery of Legendre-sparse polynomials. - Holger Rauhut; Rachel A. Ward (CISS, 2010)

Lower bounds for the error decay incurred by coarse quantization schemes - Felix Krahmer; Rachel A. Ward (CoRR, 2010)

Iterative Thresholding Meets Free-Discontinuity Problems. - Massimo Fornasier; Rachel A. Ward (Found. Comput. Math., 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)

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 001; Robert J. K. Jacob (CHI, 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 001 (HCI (20), 2011)

Computing the confidence levels for a root-mean-square test of goodness-of-fit. - William Perkins 001; Mark Tygert; Rachel A. Ward (Appl. Math. Comput., 2011)

Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization. - Massimo Fornasier; Holger Rauhut; Rachel A. Ward (SIAM J. Optim., 2011)

New and Improved Johnson-Lindenstrauss Embeddings via the Restricted Isometry Property. - Felix Krahmer; Rachel A. Ward (SIAM J. Math. Anal., 2011)

Compressive imaging: stable and robust recovery from variable density frequency samples - Felix Krahmer; Rachel A. Ward (CoRR, 2012)

Total variation minimization for stable multidimensional signal recovery - Deanna Needell; Rachel A. Ward (CoRR, 2012)

Sparse Legendre expansions via l1-minimization. - Holger Rauhut; Rachel A. Ward (J. Approx. Theory, 2012)

Weighted Eigenfunction Estimates with Applications to Compressed Sensing. - Nicolas Burq; Semyon Dyatlov; Rachel A. Ward; Maciej Zworski (SIAM J. Math. Anal., 2012)

Root-Exponential Accuracy for Coarse Quantization of Finite Frame Expansions. - Felix Krahmer; Rayan Saab; Rachel A. Ward (IEEE Trans. Inf. Theory, 2012)

Stochastic gradient descent and the randomized Kaczmarz algorithm. - Deanna Needell; Nathan Srebro; Rachel A. Ward (CoRR, 2013)

A Symbol-Based Algorithm for Decoding Bar Codes. - Mark A. Iwen; Fadil Santosa; Rachel A. Ward (SIAM J. Imaging Sci., 2013)

Stable Image Reconstruction Using Total Variation Minimization. - Deanna Needell; Rachel A. Ward (SIAM J. Imaging Sci., 2013)

Near-Optimal Compressed Sensing Guarantees for Total Variation Minimization. - Deanna Needell; Rachel A. Ward (IEEE Trans. Image Process., 2013)

Coherent Matrix Completion. - Yudong Chen 001; Srinadh Bhojanapalli; Sujay Sanghavi; Rachel A. Ward (ICML, 2014)

A unified framework for linear dimensionality reduction in l1. - Rachel A. Ward (CoRR, 2014)

Stable and Robust Sampling Strategies for Compressive Imaging. - Felix Krahmer; Rachel A. Ward (IEEE Trans. Image Process., 2014)

Relax, No Need to Round: Integrality of Clustering Formulations. - Pranjal Awasthi; Afonso S. Bandeira; Moses Charikar; Ravishankar Krishnaswamy; Soledad Villar; Rachel A. Ward (ITCS, 2015)

Recovery guarantees for exemplar-based clustering. - Abhinav Nellore; Rachel A. Ward (Inf. Comput., 2015)

Completing any low-rank matrix, provably. - Yudong Chen 001; Srinadh Bhojanapalli; Sujay Sanghavi; Rachel A. Ward (J. Mach. Learn. Res., 2015)

Compressive Sensing with Redundant Dictionaries and Structured Measurements. - Felix Krahmer; Deanna Needell; Rachel A. Ward (SIAM J. Math. Anal., 2015)

Clustering subgaussian mixtures with k-means. - Dustin G. Mixon; Soledad Villar; Rachel A. Ward (ITW, 2016)

Learning the Differential Correlation Matrix of a Smooth Function From Point Samples. - Armin Eftekhari; Ping Li 001; Michael B. Wakin; 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)

Improved bounds for sparse recovery from subsampled random convolutions. - Shahar Mendelson; Holger Rauhut; Rachel A. Ward (CoRR, 2016)

Clustering subgaussian mixtures by semidefinite programming. - Dustin G. Mixon; Soledad Villar; Rachel A. Ward (CoRR, 2016)

Batched Stochastic Gradient Descent with Weighted Sampling. - Deanna Needell; Rachel A. Ward (CoRR, 2016)

A polynomial-time relaxation of the Gromov-Hausdorff distance. - Soledad Villar; Afonso S. Bandeira; Andrew J. Blumberg; Rachel A. Ward (CoRR, 2016)

Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm. - Deanna Needell; Nathan Srebro; Rachel A. Ward (Math. Program., 2016)

One-Bit Compressive Sensing With Norm Estimation. - Karin Knudson; Rayan Saab; Rachel A. Ward (IEEE Trans. Inf. Theory, 2016)

The Sample Complexity of Weighted Sparse Approximation. - Bubacarr Bah; Rachel A. Ward (IEEE Trans. Signal Process., 2016)

Learning the second-moment matrix of a smooth function from point samples. - Armin Eftekhari; Michael B. Wakin; Ping Li 001; Paul G. Constantine; Rachel A. Ward (ACSSC, 2017)

Fast Cross-Polytope Locality-Sensitive Hashing. - Christopher Kennedy; Rachel A. Ward (ITCS, 2017)

Exact Recovery of Chaotic Systems from Highly Corrupted Data. - Giang Tran; Rachel A. Ward (Multiscale Model. Simul., 2017)

WNGrad: Learn the Learning Rate in Gradient Descent. - Xiaoxia Wu; Rachel A. Ward; Léon Bottou (CoRR, 2018)

Greedy Variance Estimation for the LASSO. - Christopher Kennedy; Rachel A. Ward (CoRR, 2018)

AdaGrad stepsizes: Sharp convergence over nonconvex landscapes, from any initialization. - Rachel A. Ward; Xiaoxia Wu; Léon Bottou (CoRR, 2018)

Recovery guarantees for polynomial approximation from dependent data with outliers. - Lam Si Tung Ho; Hayden Schaeffer; Giang Tran; Rachel A. Ward (CoRR, 2018)

Extracting Sparse High-Dimensional Dynamics from Limited Data. - Hayden Schaeffer; Giang Tran; Rachel A. Ward (SIAM J. Appl. Math., 2018)

AdaGrad stepsizes: sharp convergence over nonconvex landscapes. - Rachel A. Ward; Xiaoxia Wu; Léon Bottou (ICML, 2019)

Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network. - Xiaoxia Wu; Simon S. Du; Rachel A. Ward (CoRR, 2019)

AdaOja: Adaptive Learning Rates for Streaming PCA. - Amelia Henriksen; Rachel A. Ward (CoRR, 2019)

Bias of Homotopic Gradient Descent for the Hinge Loss. - Denali Molitor; Deanna Needell; Rachel A. Ward (CoRR, 2019)

Faster Johnson-Lindenstrauss Transforms via Kronecker Products. - Ruhui Jin; Tamara G. Kolda; Rachel A. Ward (CoRR, 2019)

Implicit Regularization of Normalization Methods. - Xiaoxia Wu; Edgar Dobriban; Tongzheng Ren; Shanshan Wu; Zhiyuan Li; Suriya Gunasekar; Rachel A. Ward; Qiang Liu 001 (CoRR, 2019)

Linear Convergence of Adaptive Stochastic Gradient Descent. - Yuege Xie; Xiaoxia Wu; Rachel A. Ward (AISTATS, 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 001 (NeurIPS, 2020)

Weighted Optimization: better generalization by smoother interpolation. - Yuege Xie; Rachel A. Ward; Holger Rauhut; Hung-Hsu Chou (CoRR, 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)

Extracting Structured Dynamical Systems Using Sparse Optimization With Very Few Samples. - Hayden Schaeffer; Giang Tran; Rachel A. Ward; Linan Zhang (Multiscale Model. Simul., 2020)

Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates. - De Huang; Jonathan Niles-Weed; Rachel A. Ward (COLT, 2021)

Bootstrapping the Error of Oja's Algorithm. - Robert Lunde; Purnamrita Sarkar; Rachel A. Ward (NeurIPS, 2021)

Function Approximation via Sparse Random Features. - Abolfazl Hashemi; Hayden Schaeffer; Robert Shi; Ufuk Topcu; Giang Tran; 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)

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning. - Yuege Xie; Bobby Shi; Hayden Schaeffer; Rachel A. Ward (CoRR, 2021)

AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method. - Xiaoxia Wu; Yuege Xie; Simon Shaolei Du; Rachel A. Ward (AAAI, 2022)

How catastrophic can catastrophic forgetting be in linear regression? - Itay Evron; Edward Moroshko; Rachel A. Ward; Nathan Srebro; Daniel Soudry (COLT, 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 (COLT, 2022)

Arbitrary-Length Analogs to de Bruijn Sequences. - Abhinav Nellore; Rachel A. Ward (CPM, 2022)

Concentration of Random Feature Matrices in High-Dimensions. - Zhijun Chen; Hayden Schaeffer; Rachel A. Ward (MSML, 2022)

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning. - Yuege Xie; Robert Shi; Hayden Schaeffer; Rachel A. Ward (MSML, 2022)

Side-effects of Learning from Low Dimensional Data Embedded in an Euclidean Space. - Juncai He; Richard Tsai 001; 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)

On the fast convergence of minibatch heavy ball momentum. - Raghu Bollapragada; Tyler Chen; Rachel A. Ward (CoRR, 2022)

AdaWAC: Adaptively Weighted Augmentation Consistency Regularization for Volumetric Medical Image Segmentation. - Yijun Dong; Yuege Xie; Rachel A. Ward (CoRR, 2022)

Matrix Concentration for Products. - De Huang; Jonathan Niles-Weed; Joel A. Tropp; Rachel A. Ward (Found. Comput. Math., 2022)

Compressed sensing with a jackknife and a bootstrap. - Aaron Defazio; Mark Tygert; Rachel A. Ward; Jure Zbontar (J. Data Sci. Stat. Vis., 2022)

Overparameterization and Generalization Error: Weighted Trigonometric Interpolation. - Yuege Xie; Hung-Hsu Chou; Holger Rauhut; Rachel A. Ward (SIAM J. Math. Data Sci., 2022)

Johnson-Lindenstrauss Embeddings with Kronecker Structure. - Stefan Bamberger; Felix Krahmer; Rachel A. Ward (SIAM J. Matrix Anal. Appl., 2022)

Sample Efficiency of Data Augmentation Consistency Regularization. - Shuo Yang; Yijun Dong; Rachel A. Ward; Inderjit S. Dhillon; Sujay Sanghavi; Qi Lei (AISTATS, 2023)

Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift. - Yijun Dong; Yuege Xie; Rachel A. Ward (ICML, 2023)

Nearly Optimal Bounds for Cyclic Forgetting. - William Swartworth; Deanna Needell; Rachel A. Ward; Mark Kong; Halyun Jeong (NeurIPS, 2023)

Convergence of Alternating Gradient Descent for Matrix Factorization. - Rachel A. Ward; Tamara G. Kolda (NeurIPS, 2023)

Robust Implicit Regularization via Weight Normalization. - Hung-Hsu Chou; Holger Rauhut; Rachel A. Ward (CoRR, 2023)

Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering. - Yijun Dong; Kevin Miller; Qi Lei; Rachel A. Ward (CoRR, 2023)

Learning to Forecast Dynamical Systems from Streaming Data. - Dimitrios Giannakis; Amelia Henriksen; Joel A. Tropp; Rachel A. Ward (SIAM J. Appl. Dyn. Syst., 2023)

Generating synthetic data for neural operators. - Erisa Hasani; Rachel A. Ward (CoRR, 2024)

Scalable Symmetric Tucker Tensor Decomposition. - Ruhui Jin; Joe Kileel 001; Tamara G. Kolda; Rachel A. Ward (SIAM J. Matrix Anal. Appl., 2024)