Preprints

(for the very latest, see my arXiv papers, google scholar papers, DBLP computer science papers, or ResearchGate and academia.edu profiles; my ORCID, and semantic scholar page)

• Global solutions in deep learning in sub-linear time, Stephen Becker (best paper award NeurIPS 2021)

• Versatile fidelity estimation with confidence, Akshay Seshadri, Martin Ringbauer, Rainer Blatt, Thomas Monz, Stephen Becker; and the companion paper (an extended version): Theory of versatile fidelity estimation with confidence by Akshay Seshadri, Martin Ringbauer, Thomas Monz, Stephen Becker

• On the computation of a non-parametric estimator by convex optimization, Akshay Seshadri, Stephen Becker

• Modeling massive multivariate spatial data with the basis graphical lasso, Mitchell Krock, William Kleiber, Dorit Hammerling, Stephen Becker

• Approximate maximum likelihood estimators for linear regression with design matrix uncertainty, Richard Clancy, Stephen Becker

• High probability convergence and uniform stability bounds for nonconvex stochastic gradient descent, Liam Madden, Emiliano Dall'Anese, Stephen Becker

Journal publications

• A study of scalar optically-pumped magnetometers for use in magnetoencephalography without shielding, Richard Clancy, Vladislav Gerginov, Orang Alem, Stephen Becker, Svenja Knappe, to appear in Physics in Medicine and Biology, link

• Spectral estimation from simulations via sketching, Zhishen Huang, Stephen Becker. In Journal of Computational Physics, link

• Stochastic Subspace Descent, David Kozak, Stephen Becker, Alireza Doostan, Luis Tenorio. Updated version is A stochastic subspace approach to gradient-free optimization in high dimensions, published in Computational Optimization and Applications, DOI version

• Bounds for the tracking error of first-order online optimization methods Liam Madden, Stephen Becker, Emiliano Dall'Anese. github code to recreate experiments. To appear in the Journal of Optimization Theory and Applications (JOTA), link.

• -regularized maximum likelihood estimation with focused-spot illumination quadruples the diffraction-limited resolution in fluorescence microscopy, J. Xing, S. Chen, S. Becker, J.-Y. Yu, C. Cogswell, Optics Express 28(26) pp 39413-39429, 2020. DOI

• Robust Least Squares for Quantized Data, Richard Clancy, Stephen Becker, in Signal Processing 176(107711) 2020, at DOI

• Randomization of Approximate Bilinear Computation for Matrix Multiplication, Osman Asif Malik and Stephen Becker, in Journal of Computer Mathematics: Computer Systems Theory (IJCM: CST) 6(1) pp 54–93, 2021. DOI link

• Resolvability of Hamming Graphs, Lucas Laird, Richard C. Tillquist, Stephen Becker, Manuel E. Lladser, in SIAM J. Discrete Math 34(4) pp 2063-2081, 2020.

• Fast Randomized Matrix and Tensor Interpolative Decomposition Using CountSketch, Osman Asif Malik and Stephen Becker. Code on github. In Advances in Computational Mathematics, vol 46 (76) 2020. Link, and DOI

• Guarantees for the Kronecker Fast Johnson-Lindenstrauss Transform Using a Coherence and Sampling Argument, Osman Asif Malik and Stephen Becker, in Linear Algebra and its Applications 602(1) pp 120-127, 2020 (DOI)

• Nonstationary Modeling With Sparsity for Spatial Data via the Basis Graphical Lasso, Mitchell Krock, William Kleiber, Stephen Becker, in Journal of Computational and Graphical Statistics, DOI. Previous arXiv version under the name “Penalized basis models for very large spatial datasets”

• Analyzing the super-resolution characteristics of focused-spot illumination approaches, J-Y Yu, V. Narumanchi, S. Chen, J. Xing, S. Becker, C. Cogswell. J. of Biomedical Optics, 25(5), 056501 (2020).

• Efficient Solvers for Sparse Subspace Clustering, Farhad Pourkamali-Anaraki, James Folberth, Stephen Becker. Signal Processing, vol 172, July 2020. DOI

• Optimization and Learning with Information Streams: Time-varying Algorithms and Applications, Emiliano Dall'Anese, Andrea Simonetto, Stephen Becker, Liam Madden, IEEE Signal Processing Magazine, vol 37(3), pp. 71–83, May 2020. DOI

• Safe Feature Elimination for Non-Negativity Constrained Convex Optimization, James Folberth, Stephen Becker. J. Optimization Theory and Applications (JOTA), 2019. Here is the post-peer-review, pre-copyedit version of the article that is published in JOTA. The final authenticated version is available online at dx.doi.org/10.1007/s10957-019-01612-w or the free view-only version.

• Improved Fixed-Rank Nystrom Approximation via QR Decomposition: Practical and Theoretical Aspects, Farhad Pourkamali-Anaraki, Stephen Becker. Neurocomputing 363(21) pp 261–272, 2019 DOI

• Stochastic Lanczos estimation of genomic variance components for linear mixed-effects models, Richard Border and Stephen Becker. BMC Bioinformatics, final version (open access), 2019

• On Quasi-Newton Forward–Backward Splitting: Proximal Calculus and Convergence, Stephen Becker, Jalal Fadili, Peter Ochs. arXiv link. SIAM J. Optimization, 29(4), 2445-2482, 2019. DOI link

• Adapting Regularized Low Rank Models for Parallel Architectures, Derek Driggs, Stephen Becker, Aleksandr Aravkin. SIAM J. Sci. Computing, 2019 (DOI link)

• Template Polyhedra and Bilinear Optimization, Jessica Gronski, Mohamed-Amin Ben Sassi, Stephen Becker, Sriram Sankaranarayanan. Formal Methods and Design, 2018.

• Achieving superresolution with illumination-enhanced sparsity, Jiun-Yann Yu, Stephen R. Becker, James Folberth, Bruce F. Wallin, Simeng Chen, and Carol J. Cogswell. Optics Express, vol 26(8) pp. 9850-9865 (2018) . Researchgate full text

• Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means, F. Pourkamali-Anaraki, S. Becker. IEEE Trans. Info Theory, 2017, DOI link. Also available via CU Scholar website.

• Efficient Adjoint Computation for Wavelet and Convolution Operators (Lecture Notes), J. Folberth and S. Becker, IEEE Signal Processing Magazine, vol 33(6) 135–147, Nov 2016 Official link or arXiv

• Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost, J. Bruer, J. Tropp, V. Cevher and S. Becker, IEEE Journal of Selected Topics in Signal Processing link, Vol 9, No 4, June 2015

• Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics, Volkan Cevher, Stephen Becker, Mark Schmidt, IEEE Signal Processing Magazine, vol. 31, no. 5, 2014. arXiv version also available

• An Algorithm for Splitting Parallel Sums of Linearly Composed Monotone Operators, with Applications to Signal Recovery, Stephen Becker and Patrick L. Combettes, Journal of Nonlinear and Convex Analysis, vol. 15, no. 1, pp. 137–159, Jan. 2014

• Improving IMRT delivery efficiency with iteratively reweighted L1-minimization for inverse planning, with Hojin Kim, Lei Xing, Ruijiang Li et al., Medical Physics, vol. 40, 071719, 2013. DOI

• A Non-Uniform Sampler for Wideband Spectrally-Sparse Environments, Michael Wakin, Stephen Becker, Eric Nakamura, Michael Grant, Emilio Sovero, Daniel Ching, Juhwan Yoo, Justin Romberg, Azita Emami-Neyestanak, Emmanuel Candès, IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), vol. 2, no. 3, pp. 516–529, 2012. DOI

• A Compressed Sensing Parameter Extraction Platform for Radar Pulse Signal Acquisition, J. Yoo, C. Turnes, E. Nakamura, C. Le, S. Becker, E. Sovero, M. Wakin, M. Grant, J. Romberg, A. Emami-Neyestanak, and E. Candès, IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), vol. 2, no. 3, pp. 626–638, 2012. DOI

• Dynamical Behavior Near a Liquid-Liquid Phase Transition in Simulations of Supercooled Water by P. H. Poole, S. R. Becker, F. Sciortino, and F. W. Starr; Journal of Physical Chemistry B, vol. 114, no. 48, pp. 14176–14183, 2011.

• Templates for Convex Cone Problems with Applications to Sparse Signal Recovery by S. Becker, E. Candès, M. Grant. Mathematical Programming Computation, vol 3 no 3 (2011) 165–218. Software. An extended version appears as chapter 4 in my thesis. Typos: eq. 4.6a should have 1, not sigma_k^{-1}t_k^{(1)}, in the second parameter of Trunc, and the two equations above should have z^{(1)} not z^{(2)} in the l-infinity norm constraint. Listing 6 should have the adjoint of W in line 4.

• NESTA: a fast and accurate first-order method for sparse recovery by S. Becker, J. Bobin and E. J. Candès. SIAM J. on Imaging Sciences (SIIMS), vol 4 no. 1 (2011). Software. An extended version appears as chapter 2 in my thesis.

• Quantum state tomography via compressed sensing by Gross, Liu, Flammia, Becker and Eisert, in Physical Review Letters, 2010, vol 105, number 15 (4 pages).

• Relation between the Widom line and the breakdown of the Stokes-Einstein relation in supercooled water, P. Kumar, S.V. Buldyrev, S.R. Becker, P.H. Poole, F.W. Starr, and H.E. Stanley, Proceedings of the National Academy of Science, Vol. 104, 9575-9579 (2007).

• Fractional Stokes-Einstein and Debye-Stokes-Einstein Relations in a Network-Forming Liquid, by Stephen R. Becker, Peter H. Poole, and Francis W. Starr, in Physical Review Letters, vol. 97 no. 5. Copyright 2006 by the American Physical Society.

• “The Dynamics of Falling Dominoes”, Stan Wagon, Adrianne Pontarelli, Stephen Becker and William Briggs, UMAP Journal, (26)1, 2005, pp. 37-48.

Book chapters

• Dual Smoothing Techniques for Variational Matrix Decomposition, S. Becker and A. Aravkin, in “Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing”, T. Bouwmans, N. Aybat, E. Zahzah, eds. CRC Press, 2016. arXiv link

Conference publications

• Superresolution photoacoustic tomography using random speckle illumination and second order moments, Osman Asif Malik, Venkatalakshmi Vyjayanthi Narumanchi, Stephen Becker, Todd W. Murray. In WASPAA conference, 2021

• A Sampling Based Method for Tensor Ring Decomposition, Osman Asif Malik, Stephen Becker. In ICML 2021

• Nuclear Norm Based Spectrum Estimation for Molecular Dynamic Simulations, Shuang Li, Stephen Becker, Michael Wakin. In Asilomar conference on Signals, Systems and Computers, 2020.

• Stochastic Gradient Langevin Dynamics with Variance Reduction, Zhishen Huang, Stephen Becker. In IJCNN2021 (International Joint Conference on Neural Networks)

• Computational super-resolution microscopy: leveraging noise model, regularization and sparsity to achieve highest resolution, Jian Xing, Simeng Chen, Stephen Becker, Jiun-Yann Yu and Carol Cogswell, in SPIE BiOS 2020, San Francisco, CA. DOI link

• One-Pass Sparsified Gaussian Mixtures, Eric Kightley and Stephen Becker, in the Machine Learning workshop, at the 2019 IEEE International Conference on Big Data (BigData 2019), Dec 9-12, 2019, Los Angeles, CA, USA (“special session” on Machine Learning in Big Data). Paper available on arXiv.

• Online Sparse Subspace Clustering, Liam Madden, Stephen Becker, Emiliano Dall'Anese (in the 2nd IEEE Data Science Workshop, June 2019 Minneapolis). DOI link

• Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions, Zhishen (Leo) Huang and Stephen Becker, in INNS Big Data and Deep Learning 2019 (Sestri Levante, Genova, Italy 16-18 April 2019). Published version appears in Recent Advances in Big Data and Deep Learning. DOI link

• Low-rank Tucker decomposition of large tensors using TensorSketch, S. Becker and O. Malik, in NIPS 2018 (aka NeurIPS 2018). Code on github

• Randomized Clustered Nystrom for Large-Scale Kernel Machines, F. Pourkamali-Anaraki, S. Becker, M. Wakin. Conference on Artificial Intelligence (AAAI 2018), San Francisco, Feb 2018. (A preliminary, shorter version appears as Randomized Clustered Nystrom for Large-Scale Kernel Machines, Farhad Pourkamali-Anaraki, Stephen Becker )

• Estimating Active Subspaces with Randomized Gradient Sampling, S Becker and F. Pourkamali-Anaraki, SIAM Workshop on Dimension Reduction 2017, July 9-10, Pittsburg. Also available via CU Scholar website.

• Robust Partially-Compressed Least-Squares, S. Becker, B. Kawas, M. Petrik, K. N. Ramamurthy. In AAAI 2017

• A Randomized Approach to Efficient Kernel Clustering, F. Pourkamali-Anaraki, S. Becker, in 2016 IEEE GlobalSIP (Global Conference on Signal and Information Processing). Also available via CU Scholar website.

• Efficient Dictionary Learning via Very Sparse Random Projections, F. Pourkamali-Anaraki, S. Becker, S. Hughes, in SampTA (May 2015). DOI link

• General Optimization Framework for Robust and Regularized 3D FWI, S. Becker, L. Horesh, A. Aravkin, E. van den Berg, S. Zhuk, as extended abstract at EAGE (Madrid, Spain, June 2015)

• Time–Data Tradeoffs by Aggressive Smoothing, J. Bruer, J. A. Tropp, V. Cevher, S. Becker, NIPS 2014, Montreal.

• QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models, C-J. Hsieh, I. Dhillon, P. Ravikumar, S. Becker, P. Olsen, NIPS 2014, Montreal.

• A variational approach to stable principal component pursuit, A. Aravkin, S. Becker, V. Cevher, P. Olsen, UAI 2014. PDF

• Joint low-rank representation and matrix completion under a singular value thresholding framework, C. Tzagkarakis, S. Becker and A. Mouchtaris, EUSIPCO 2014.

• Metric learning with rank and sparsity constraints, B. Bah, S. Becker, V. Cevher and B. Gozcu, ICASSP 2014.

• A proximal splitting method for inf-convolutive variational models in image recovery, S. Becker and P. L. Combettes, accepted in ICIP 2013, Melbourne

• Scalable and accurate quantum tomography from fewer measurements, Stephen Becker and Volkan Cevher, SPARS 2013, Lausanne Switzerland.

• Randomized Singular Value Projection, Stephen Becker, Volkan Cevher, Anastasios Kyrillidis, SampTA 2013, Bremen Germany. This arXiv version is an extended version (i.e., with proofs) of the 4 page SampTA proceedings version.

• Sparse projections onto the simplex, Stephen Becker, Volkan Cevher, Christopher Koch, Anastasios Kyrillidis (the v3 on arXiv is our expanded version of our NIPS conference version). ICML, Atlanta. For fun, see the ICML 2013 review boards. There is an earlier version on the JMLR website.

• Sparse projections onto the simplex, Anastasios Kyrillidis, Stephen Becker, Volkan Cevher, in NIPS Workshop on Discrete Optimization in Machine Learning (DISCML), Dec. 2012

• A quasi-Newton proximal splitting method, Stephen Becker, M. Jalal Fadili, in NIPS 2012 (Lake Tahoe) (recieved a “spotlight presentation” as one of the top 5% of submissions)

• A 100MHz-2GHz 12.5x sub-Nyquist Rate Receiver in 90nm CMOS, J. Yoo, S. Becker, M. Loh, M. Monge, E. Candès, A. Emami-Neyestanak. In RFIC 2012 (Montreal, Canada, June 2012). DOI

• Design and implementation of a fully integrated compressed-sensing signal acquisition system, J. Yoo, S. Becker, M. Monge, M. Loh, E. Candès, A. Emami-Neyestanak. In ICASSP 2012 (Kyoto, Japan, March 2012) DOI

Selected Talks

(full list of invited talks is on my CV; here I list a few that have video or slides or websites)

• “Matrix Completion and Robust PCA”, University of Colorado Boulder, Computer Science department colloquium, Boulder, CO. Nov 20 2014. Video available here

• “Advances in first-order methods: constraints, non-smoothness and faster convergence”, Minisymposium, SIAM Imaging Science, Philadelphia. Slides, May 22 2012

• “TFOCS: Flexible First-order Methods for Rank Minimization”, at the Low-rank Matrix Optimization minisymposium at the 2011 SIAM Conference on Optimization in Darmstadt. Here's the RPCA video (Matlab code to generate this can be found at the Demo page on the TFOCS website or at the locally hosted version of the RPCA demo), May 19 2011

• “Quick intro to convex optimization” talk for Patrick Sanan's department “ACM^tea”, Oct 23 2009

Technical Reports

• Locality-sensitive hashing in function spaces, Will Shand and Stephen Becker

• Tensor Robust Principal Component Analysis: Better recovery with atomic norm regularization, Derek Driggs, Stephen Becker and Jordan Boyd-Graber

• URV Factorization with Random Orthogonal System Mixing, Stephen Becker, James Folberth, Laura Grigori

August 2019

The Chen-Teboulle algorithm is the proximal point algorithm, from 2011 but posted 2019, shows the Chen-Teboulle algorithm admits a more aggressive stepsize than via the original analysis.

May 2016

Exact linesearch for LASSO discusses exact step-size selection for piecewise quadratic objective functions, with code at github

Theses

April 2011

Practical Compressed Sensing: modern data acquisition and signal processing, California Institute of Technology (PhD thesis). Co-winner, Carey prize.

April 2005

Translational and Rotational Dynamics of Supercooled Water, Wesleyan University (undergraduate thesis). Co-winner, Bertman prize.

Theses of my students (after 2019, CU stopped posting these online, so I'm posting local copies when I haven them):

• In the pipeline

  • Akshay Seshadri (PhD, CU Physics) 2023?

  • Kevin Doherty (PhD), 2023-4?

  • Jacob Spainhour (PhD), 2024?

  • Cooper Simpson (MS), 2022 summer

• A Generalization of S-Divergence to Symmetric Cone Programming via Euclidean Jordan Algebra (local copy), Zhuochen (Jaden) Wang, 2022, BS

• Mathematical Formulations with Uncertain Data in Optimization and Inverse Problems Richard Clancy, 2022, PhD

• Liam Madden, 2022, PhD (co-advised with Emiliano Dall'Anese, CU ECEE)

• Measuring Image Resolution in Super-Resolution Microscopy and Bayesian Estimation of Population Size and Overlap and Vaccine Effectiveness Erik Johnson, 2021, PhD (main advisor Dan Larremore, CU CS)

• Topics in Matrix and Tensor Computations Osman Malik, 2021, PhD. local PDF copy

• Jacob Tiede, 2021, Professional Masters, culminating experience

• Randomization in Statistical Machine Learning Zhishen (Leo) Huang, 2020, PhD. local PDF copy

• Iterative stochastic optimization for large-scale machine learning and statistical inverse problems, David Kozak, 2020, PhD (main advisor: Luis Tenorio, Colorado School of Mines, Applied Math & Stat)

• A comparison of spectral estimation techniques, Marc Thomson, 2019, Masters; code at github.

• Stokes, Gauss, and Bayes walk into a bar…, Eric Kightley, 2019, PhD

• Non-Convex Optimization and Applications to Bilinear Programming and Super-Resolution Imaging, Jessica Gronski, 2019, PhD

• Stochastic Lanczos Likelihood Estimation of Genomic Variance Components, Richard Border, 2018, Masters

• Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing, James Folberth, 2018, PhD

• Randomized Algorithms for Large-Scale Data Analysis, Farhad Pourkamali Anaraki, 2017, PhD

• Optimization for High-Dimensional Data Analysis, Derek Driggs, 2017, Masters

Miscellany

• Open Problem: Property Elicitation and Elicitation Complexity, Rafael Frongillo, Ian Kash, Stephen Becker. 29th Annual Conference on Learning Theory (COLT), pp. 1655–1658, 2016

• Trig identities that I used in high school and college

Copyright information

Please support journals that allow authors to post their final papers.

• SIAM “Information for Authors”. On their copyright transfer form, they state that the author retains the right to post the final paper on their academic website, as long as the SIAM copyright notice is posted and no fee is charged.

• American Physical Society (APS) posting policy. APS publishes the Physical Review series. Summary: authors can post the paper as long as the citation and APS copyright are included, and no fee is charged. The copyright and citation look like: “Authors names, journal title, volume number, page number (or article identifier), year of publication. ”Copyright (year) by the American Physical Society.“”

• IEEE Electronic Information Dissemination page. Authors can post a published paper, but need to include the IEEE copyright notice and full citation (see the link for the copyright notice).

The SHERPA/ROMEO website lists the policies of many publishers. The SPARC addendum is an addendum to copyright agreements that authors may request their publisher to sign.