Templates for convex cone problems with applications to sparse signal recovery. - Stephen Becker; Emmanuel J. Candès; Michael C. Grant (Math. Program. Comput., 2011)

NESTA: A Fast and Accurate First-Order Method for Sparse Recovery. - Stephen Becker; Jérôme Bobin; Emmanuel J. Candès (SIAM J. Imaging Sci., 2011)

Design and implementation of a fully integrated compressed-sensing signal acquisition system. - Juhwan Yoo; Stephen Becker; Manuel Monge; Matthew Loh; Emmanuel J. Candès; Azita Emami-Neyestanak (ICASSP, 2012)

A quasi-Newton proximal splitting method. - Stephen Becker; Mohamed-Jalal Fadili (NIPS, 2012)

Sparse projections onto the simplex - Anastasios Kyrillidis; Stephen Becker; Volkan Cevher (CoRR, 2012)

A Nonuniform Sampler for Wideband Spectrally-Sparse Environments. - Michael B. Wakin; Stephen Becker; Eric B. Nakamura; Michael C. Grant; Emilio A. Sovero; Daniel Ching; Juhwan Yoo; Justin K. Romberg; Azita Emami-Neyestanak; Emmanuel J. Candès (IEEE J. Emerg. Sel. Topics Circuits Syst., 2012)

A Compressed Sensing Parameter Extraction Platform for Radar Pulse Signal Acquisition. - Juhwan Yoo; Christopher K. Turnes; Eric B. Nakamura; Chi K. Le; Stephen Becker; Emilio A. Sovero; Michael B. Wakin; Michael C. Grant; Justin K. Romberg; Azita Emami-Neyestanak; Emmanuel J. Candès (IEEE J. Emerg. Sel. Topics Circuits Syst., 2012)

Compressive sensing: Principles and hardware implementations. - Emmanuel J. Candès; Stephen Becker (ESSCIRC, 2013)

Sparse simplex projections for portfolio optimization. - Anastasios Kyrillidis; Stephen Becker; Volkan Cevher; Christoph Koch 001 (GlobalSIP, 2013)

Sparse projections onto the simplex. - Anastasios Kyrillidis; Stephen Becker; Volkan Cevher; Christoph Koch 001 (ICML (2), 2013)

Randomized Low-Memory Singular Value Projection - Stephen Becker; Volkan Cevher; Anastasios Kyrillidis (CoRR, 2013)

Joint low-rank representation and matrix completion under a singular value thresholding framework. - Christos Tzagkarakis; Stephen Becker; Athanasios Mouchtaris (EUSIPCO, 2014)

Metric learning with rank and sparsity constraints. - Bubacarr Bah; Stephen Becker; Volkan Cevher; Baran Gozcu (ICASSP, 2014)

Time-Data Tradeoffs by Aggressive Smoothing. - John J. Bruer; Joel A. Tropp; Volkan Cevher; Stephen Becker (NIPS, 2014)

QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models. - Cho-Jui Hsieh; Inderjit S. Dhillon; Pradeep Ravikumar; Stephen Becker; Peder A. Olsen (NIPS, 2014)

A variational approach to stable principal component pursuit. - Aleksandr Y. Aravkin; Stephen Becker; Volkan Cevher; Peder A. Olsen (UAI, 2014)

Convex Optimization for Big Data. - Volkan Cevher; Stephen Becker; Mark Schmidt 001 (CoRR, 2014)

Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics. - Volkan Cevher; Stephen Becker; Mark Schmidt 001 (IEEE Signal Process. Mag., 2014)

Efficient Dictionary Learning via Very Sparse Random Projections. - Farhad Pourkamali-Anaraki; Stephen Becker; Shannon M. Hughes (CoRR, 2015)

Robust Partially-Compressed Least-Squares. - Stephen Becker; Ban Kawas; Marek Petrik; Karthikeyan Natesan Ramamurthy (CoRR, 2015)

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost. - John J. Bruer; Joel A. Tropp; Volkan Cevher; Stephen Becker (IEEE J. Sel. Top. Signal Process., 2015)

Open Problem: Property Elicitation and Elicitation Complexity. - Rafael M. Frongillo; Ian A. Kash; Stephen Becker (COLT, 2016)

A randomized approach to efficient kernel clustering. - Farhad Pourkamali-Anaraki; Stephen Becker (GlobalSIP, 2016)

Randomized Clustered Nystrom for Large-Scale Kernel Machines. - Farhad Pourkamali-Anaraki; Stephen Becker (CoRR, 2016)

Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition. - Aleksandr Y. Aravkin; Stephen Becker (CoRR, 2016)

Efficient Adjoint Computation for Wavelet and Convolution Operators [Lecture Notes]. - James Folberth; Stephen Becker (IEEE Signal Process. Mag., 2016)

Robust Partially-Compressed Least-Squares. - Stephen Becker; Ban Kawas; Marek Petrik (AAAI, 2017)

Preconditioned Data Sparsification for Big Data With Applications to PCA and K-Means. - Farhad Pourkamali-Anaraki; Stephen Becker (IEEE Trans. Inf. Theory, 2017)

Randomized Clustered Nystrom for Large-Scale Kernel Machines. - Farhad Pourkamali-Anaraki; Stephen Becker; Michael B. Wakin (AAAI, 2018)

Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch. - Osman Asif Malik; Stephen Becker (NeurIPS, 2018)

Efficient Solvers for Sparse Subspace Clustering. - Farhad Pourkamali-Anaraki; Stephen Becker (CoRR, 2018)

One-Pass Sparsified Gaussian Mixtures. - Eric Kightley; Stephen Becker (IEEE BigData, 2019)

Online Sparse Subspace Clustering. - Liam Madden; Stephen Becker; Emiliano Dall'Anese (DSW, 2019)

Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions. - Zhishen Huang; Stephen Becker (INNSBDDL, 2019)

Stochastic Lanczos estimation of genomic variance components for linear mixed-effects models. - Richard Border; Stephen Becker (BMC Bioinform., 2019)

Guarantees for the Kronecker Fast Johnson-Lindenstrauss Transform Using a Coherence and Sampling Argument. - Osman Asif Malik; Stephen Becker (CoRR, 2019)

Template polyhedra and bilinear optimization. - Jessica A. Gronski; Mohamed Amin Ben Sassi; Stephen Becker; Sriram Sankaranarayanan 001 (Formal Methods Syst. Des., 2019)

Improved fixed-rank Nyström approximation via QR decomposition: Practical and theoretical aspects. - Farhad Pourkamali-Anaraki; Stephen Becker (Neurocomputing, 2019)

On Quasi-Newton Forward-Backward Splitting: Proximal Calculus and Convergence. - Stephen Becker; Jalal Fadili; Peter Ochs (SIAM J. Optim., 2019)

Adapting Regularized Low-Rank Models for Parallel Architectures. - Derek Driggs; Stephen Becker; Aleksandr Y. Aravkin (SIAM J. Sci. Comput., 2019)

Nuclear Norm Based Spectrum Estimation for Molecular Dynamic Simulations. - Shuang Li 003; Stephen Becker; Michael B. Wakin (ACSSC, 2020)

Fast randomized matrix and tensor interpolative decomposition using CountSketch. - Osman Asif Malik; Stephen Becker (Adv. Comput. Math., 2020)

Locality-sensitive hashing in function spaces. - Will Shand; Stephen Becker (CoRR, 2020)

Robust Least Squares for Quantized Data. - Richard Clancy; Stephen Becker (CoRR, 2020)

Safe Feature Elimination for Non-negativity Constrained Convex Optimization. - James Folberth; Stephen Becker (J. Optim. Theory Appl., 2020)

Resolvability of Hamming Graphs. - Lucas Laird; Richard C. Tillquist; Stephen Becker; Manuel E. Lladser (SIAM J. Discret. Math., 2020)

Efficient Solvers for Sparse Subspace Clustering. - Farhad Pourkamali-Anaraki; James Folberth; Stephen Becker (Signal Process., 2020)

Robust least squares for quantized data matrices. - Stephen Becker; Richard Clancy (Signal Process., 2020)

Optimization and Learning With Information Streams: Time-varying algorithms and applications. - Emiliano Dall'Anese; Andrea Simonetto; Stephen Becker; Liam Madden (IEEE Signal Process. Mag., 2020)

A Sampling-Based Method for Tensor Ring Decomposition. - Osman Asif Malik; Stephen Becker (ICML, 2021)

Stochastic Gradient Langevin Dynamics with Variance Reduction. - Zhishen Huang; Stephen Becker (IJCNN, 2021)

Superresolution Photoacoustic Tomography Using Random Speckle Illumination and Second Order Moments. - Osman Asif Malik; Venkatalakshmi Vyjayanthi Narumanchi; Stephen Becker; Todd W. Murray (WASPAA, 2021)

Call for Special Issue Papers: Big Scientific Data and Machine Learning in Science and Engineering: Deadline for Manuscript Submission: February 1, 2022. - Farhad Pourkamali-Anaraki; Mohammad Amin Hariri-Ardebili; Stephen Becker; Maziar Raissi (Big Data, 2021)

A stochastic subspace approach to gradient-free optimization in high dimensions. - David Kozak; Stephen Becker; Alireza Doostan; Luis Tenorio (Comput. Optim. Appl., 2021)

Modeling massive multivariate spatial data with the basis graphical lasso. - Mitchell Krock; William Kleiber; Dorit Hammerling; Stephen Becker (CoRR, 2021)

Approximate maximum likelihood estimators for linear regression with design matrix uncertainty. - Richard J. Clancy; Stephen Becker (CoRR, 2021)

Randomization of approximate bilinear computation for matrix multiplication. - Osman Asif Malik; Stephen Becker (Int. J. Comput. Math. Comput. Syst. Theory, 2021)

Nonstationary Modeling With Sparsity for Spatial Data via the Basis Graphical Lasso. - Mitchell Krock; William Kleiber; Stephen Becker (J. Comput. Graph. Stat., 2021)

Spectral estimation from simulations via sketching. - Zhishen Huang; Stephen Becker (J. Comput. Phys., 2021)

Bounds for the Tracking Error of First-Order Online Optimization Methods. - Liam Madden; Stephen Becker; Emiliano Dall'Anese (J. Optim. Theory Appl., 2021)

Fast Algorithms for Monotone Lower Subsets of Kronecker Least Squares Problems. - Osman Asif Malik; Yiming Xu; Nuojin Cheng; Stephen Becker; Alireza Doostan; Akil Narayan 001 (CoRR, 2022)

Quadrature Sampling of Parametric Models with Bi-fidelity Boosting. - Nuojin Cheng; Osman Asif Malik; Yiming Xu; Stephen Becker; Alireza Doostan; Akil Narayan 001 (CoRR, 2022)

QCNN: Quadrature Convolutional Neural Network with Application to Unstructured Data Compression. - Kevin Doherty; Cooper Simpson; Stephen Becker; Alireza Doostan (CoRR, 2022)

Bi-fidelity Variational Auto-encoder for Uncertainty Quantification. - Nuojin Cheng; Osman Asif Malik; Stephen Becker; Alireza Doostan (CoRR, 2023)

In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD. - Riccardo Balin; Filippo Simini; Cooper Simpson; Andrew Shao; Alessandro Rigazzi; Matthew Ellis; Stephen Becker; Alireza Doostan; John A. Evans; Kenneth E. Jansen (CoRR, 2023)

Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso. - Mitchell Krock; William Kleiber; Dorit Hammerling; Stephen Becker (J. Comput. Graph. Stat., 2023)

Variational Entropy Search for Adjusting Expected Improvement. - Nuojin Cheng; Stephen Becker (CoRR, 2024)

Optimization of Array Encoding for Ultrasound Imaging. - Jacob Spainhour; Korben Smart; Stephen Becker; Nick Bottenus (CoRR, 2024)

Online randomized interpolative decomposition with a posteriori error estimator for temporal PDE data reduction. - Angran Li; Stephen Becker; Alireza Doostan (CoRR, 2024)

High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise. - Liam Madden; Emiliano Dall'Anese; Stephen Becker (J. Mach. Learn. Res., 2024)

Subsampling of Parametric Models with Bifidelity Boosting. - Nuojin Cheng; Osman Asif Malik; Yiming Xu; Stephen Becker; Alireza Doostan; Akil Narayan 001 (SIAM/ASA J. Uncertain. Quantification, 2024)

QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression. - Kevin Doherty; Cooper Simpson; Stephen Becker; Alireza Doostan (J. Comput. Phys., 2024)

Aligning to What? Limits to RLHF Based Alignment. - Logan Barnhart; Reza Akbarian Bafghi; Stephen Becker; Maziar Raissi (NAACL (Findings), 2025)

Exploring Exploration in Bayesian Optimization. - Leonard Papenmeier; Nuojin Cheng; Stephen Becker; Luigi Nardi (UAI, 2025)

Dependence of parallel imaging with linear predictability on the undersampling direction. - Alex McManus; Stephen Becker; Nicholas Dwork (J. Electronic Imaging, 2025)

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation. - Bhavana Jonnalagadda; Stephen Becker (CoRR, 2025)

A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization. - Nuojin Cheng; Leonard Papenmeier; Stephen Becker; Luigi Nardi (CoRR, 2025)

WENDy for Nonlinear-in-Parameters ODEs. - Nic Rummel; Daniel A. Messenger; Stephen Becker; Vanja Dukic; David M. Bortz (CoRR, 2025)

Approximate maximum likelihood estimators for linear regression with independent component-wise design matrix uncertainty. - Richard J. Clancy; Stephen Becker (Math. Program. Comput., 2025)

Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search. - Nuojin Cheng; Alireza Doostan; Stephen Becker (CoRR, 2025)