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ORCIDMichael P. Friedlander
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- affiliation: University of British Columbia, Department of Computer Science, Vancouver, BC, Canada
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Journal Articles
- 2024
[j33]Zhenan Fan, Huang Fang, Michael P. Friedlander:
Cardinality-constrained structured data-fitting problems. Open J. Math. Optim. 5: 1-21 (2024)- 2023
- [j32]Michael P. Friedlander
, Ariel Goodwin, Tim Hoheisel:
From Perspective Maps to Epigraphical Projections. Math. Oper. Res. 48(3): 1711-1740 (2023) - 2022
- [j31]Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander:
Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case. J. Mach. Learn. Res. 23: 121:1-121:38 (2022) - [j30]Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, Pooya Ronagh:
Quantum algorithms for structured prediction. Quantum Mach. Intell. 4(2): 1-25 (2022) - [j29]Michael P. Friedlander, Halyun Jeong, Yaniv Plan, Özgür Yilmaz:
NBIHT: An Efficient Algorithm for 1-Bit Compressed Sensing With Optimal Error Decay Rate. IEEE Trans. Inf. Theory 68(2): 1157-1177 (2022) - [j28]Zhenan Fan, Halyun Jeong, Babhru Joshi, Michael P. Friedlander:
Polar Deconvolution of Mixed Signals. IEEE Trans. Signal Process. 70: 2713-2727 (2022) - 2020
- [j27]Zhenan Fan, Halyun Jeong, Yifan Sun, Michael P. Friedlander:
Atomic Decomposition via Polar Alignment: The Geometry of Structured Optimization. Found. Trends Optim. 3(4): 280-366 (2020) - [j26]Ron Estrin, Michael P. Friedlander:
A perturbation view of level-set methods for convex optimization. Optim. Lett. 14(8): 1989-2006 (2020) - [j25]Ron Estrin, Michael P. Friedlander, Dominique Orban, Michael A. Saunders:
Implementing a Smooth Exact Penalty Function for Equality-Constrained Nonlinear Optimization. SIAM J. Sci. Comput. 42(3): A1809-A1835 (2020) - [j24]Ron Estrin, Michael P. Friedlander, Dominique Orban, Michael A. Saunders:
Implementing a Smooth Exact Penalty Function for General Constrained Nonlinear Optimization. SIAM J. Sci. Comput. 42(3): A1836-A1859 (2020) - 2019
- [j23]Aleksandr Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy, Michael P. Friedlander, Scott Roy:
Level-set methods for convex optimization. Math. Program. 174(1-2): 359-390 (2019) - [j22]Michael P. Friedlander, Ives Macêdo, Ting Kei Pong:
Polar Convolution. SIAM J. Optim. 29(2): 1366-1391 (2019) - 2018
- [j21]Aleksandr Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy, Michael P. Friedlander, Kellie J. MacPhee:
Foundations of Gauge and Perspective Duality. SIAM J. Optim. 28(3): 2406-2434 (2018) - 2016
- [j20]Michael P. Friedlander, Nathan Krislock, Ting Kei Pong:
Social Resistance. Comput. Sci. Eng. 18(2): 98-103 (2016) - [j19]Michael P. Friedlander, Ives Macêdo:
Low-Rank Spectral Optimization via Gauge Duality. SIAM J. Sci. Comput. 38(3) (2016) - 2014
- [j18]Michael P. Friedlander, Ives Macêdo, Ting Kei Pong:
Gauge Optimization and Duality. SIAM J. Optim. 24(4): 1999-2022 (2014) - 2013
- [j17]Aleksandr Y. Aravkin, James V. Burke, Michael P. Friedlander:
Variational Properties of Value Functions. SIAM J. Optim. 23(3): 1689-1717 (2013) - [j16]Michael P. Friedlander, Mark Schmidt:
Erratum: Hybrid Deterministic-Stochastic Methods for Data Fitting. SIAM J. Sci. Comput. 35(4) (2013) - 2012
- [j15]Aleksandr Y. Aravkin, Michael P. Friedlander, Felix J. Herrmann, Tristan van Leeuwen:
Robust inversion, dimensionality reduction, and randomized sampling. Math. Program. 134(1): 101-125 (2012) - [j14]Michael P. Friedlander, Dominique Orban:
A primal-dual regularized interior-point method for convex quadratic programs. Math. Program. Comput. 4(1): 71-107 (2012) - [j13]Michael P. Friedlander, Mark Schmidt:
Hybrid Deterministic-Stochastic Methods for Data Fitting. SIAM J. Sci. Comput. 34(3) (2012) - [j12]Felix J. Herrmann, Michael P. Friedlander, Özgür Yilmaz:
Fighting the Curse of Dimensionality: Compressive Sensing in Exploration Seismology. IEEE Signal Process. Mag. 29(3): 88-100 (2012) - [j11]Michael P. Friedlander, Hassan Mansour, Rayan Saab, Özgür Yilmaz:
Recovering Compressively Sampled Signals Using Partial Support Information. IEEE Trans. Inf. Theory 58(2): 1122-1134 (2012) - 2011
- [j10]Ewout van den Berg, Michael P. Friedlander:
Sparse Optimization with Least-Squares Constraints. SIAM J. Optim. 21(4): 1201-1229 (2011) - 2010
- [j9]Ewout van den Berg, Michael P. Friedlander:
Theoretical and empirical results for recovery from multiple measurements. IEEE Trans. Inf. Theory 56(5): 2516-2527 (2010) - 2009
- [j8]Ewout van den Berg, Michael P. Friedlander, Gilles Hennenfent, Felix J. Herrmann, Rayan Saab, Özgür Yilmaz:
Algorithm 890: Sparco: A Testing Framework for Sparse Reconstruction. ACM Trans. Math. Softw. 35(4): 29:1-29:16 (2009) - 2008
- [j7]Michael P. Friedlander, Kathrin Hatz:
Computing non-negative tensor factorizations. Optim. Methods Softw. 23(4): 631-647 (2008) - [j6]Michael P. Friedlander, Sven Leyffer:
Global and Finite Termination of a Two-Phase Augmented Lagrangian Filter Method for General Quadratic Programs. SIAM J. Sci. Comput. 30(4): 1706-1729 (2008) - [j5]Ewout van den Berg, Michael P. Friedlander:
Probing the Pareto Frontier for Basis Pursuit Solutions. SIAM J. Sci. Comput. 31(2): 890-912 (2008) - 2007
- [j4]Michael P. Friedlander, Paul Tseng:
Exact Regularization of Convex Programs. SIAM J. Optim. 18(4): 1326-1350 (2007) - 2006
- [j3]Michael P. Friedlander, M. R. Gupta:
On minimizing distortion and relative entropy. IEEE Trans. Inf. Theory 52(1): 238-245 (2006) - 2005
- [j2]Michael P. Friedlander, Michael A. Saunders:
A Globally Convergent Linearly Constrained Lagrangian Method for Nonlinear Optimization. SIAM J. Optim. 15(3): 863-897 (2005) - [j1]Victor DeMiguel, Michael P. Friedlander, Francisco J. Nogales, Stefan Scholtes:
A two-sided relaxation scheme for Mathematical Programs with Equilibrium Constraints. SIAM J. Optim. 16(2): 587-609 (2005)
Conference and Workshop Papers
- 2024
- [c13]Zhenan Fan, Huang Fang, Xinglu Wang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang:
Fair and Efficient Contribution Valuation for Vertical Federated Learning. ICLR 2024 - 2022
- [c12]Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, Yong Zhang:
Improving Fairness for Data Valuation in Horizontal Federated Learning. ICDE 2022: 2440-2453 - 2021
- [c11]Huang Fang, Zhenan Fan, Michael P. Friedlander:
Fast convergence of stochastic subgradient method under interpolation. ICLR 2021 - 2020
- [c10]Huang Fang, Zhenan Fan, Yifan Sun, Michael P. Friedlander:
Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization. AISTATS 2020: 434-444 - [c9]Huang Fang, Nick Harvey, Victor S. Portella, Michael P. Friedlander:
Online mirror descent and dual averaging: keeping pace in the dynamic case. ICML 2020: 3008-3017 - 2019
- [c8]Zhenan Fan, Yifan Sun, Michael P. Friedlander:
Bundle methods for dual atomic pursuit. ACSSC 2019: 264-270 - [c7]Yifan Sun, Michael P. Friedlander:
One-shot atomic detection. CAMSAP 2019: 1-5 - [c6]Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael P. Friedlander:
Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization. SDM 2019: 280-288 - 2016
- [c5]Gabriel Goh, Andrew Cotter, Maya R. Gupta, Michael P. Friedlander:
Satisfying Real-world Goals with Dataset Constraints. NIPS 2016: 2415-2423 - 2015
- [c4]Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke:
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. ICML 2015: 1632-1641 - 2013
- [c3]Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias W. Seeger:
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML (3) 2013: 951-959 - 2012
- [c2]Aleksandr Y. Aravkin, Michael P. Friedlander, Tristan van Leeuwen:
Robust inversion via semistochastic dimensionality reduction. ICASSP 2012: 5245-5248 - 2009
- [c1]Mark Schmidt, Ewout van den Berg, Michael P. Friedlander, Kevin P. Murphy:
Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. AISTATS 2009: 456-463
Informal and Other Publications
- 2022
- [i21]Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang:
Fair and efficient contribution valuation for vertical federated learning. CoRR abs/2201.02658 (2022) - [i20]Zhenan Fan, Huang Fang, Michael P. Friedlander:
A dual approach for federated learning. CoRR abs/2201.11183 (2022) - [i19]Zhenan Fan, Zirui Zhou, Jian Pei, Michael P. Friedlander, Jiajie Hu, Chengliang Li, Yong Zhang:
Knowledge-Injected Federated Learning. CoRR abs/2208.07530 (2022) - 2021
- [i18]Michael P. Friedlander, Ariel Goodwin, Tim Hoheisel:
From perspective maps to epigraphical projections. CoRR abs/2102.06809 (2021) - [i17]Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, Yong Zhang:
Improving Fairness for Data Valuation in Federated Learning. CoRR abs/2109.09046 (2021) - 2020
- [i16]Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander:
Online mirror descent and dual averaging: keeping pace in the dynamic case. CoRR abs/2006.02585 (2020) - [i15]Zhenan Fan, Halyun Jeong, Babhru Joshi, Michael P. Friedlander:
Polar Deconvolution of Mixed Signals. CoRR abs/2010.10508 (2020) - [i14]Michael P. Friedlander, Halyun Jeong, Yaniv Plan, Özgür Yilmaz:
NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate. CoRR abs/2012.12886 (2020) - 2019
- [i13]Ron Estrin, Michael P. Friedlander, Dominique Orban, Michael A. Saunders:
Implementing a smooth exact penalty function for equality-constrained nonlinear optimization. CoRR abs/1910.04300 (2019) - [i12]Ron Estrin, Michael P. Friedlander, Dominique Orban, Michael A. Saunders:
Implementing a smooth exact penalty function for general constrained nonlinear optimization. CoRR abs/1912.02093 (2019) - [i11]Zhenan Fan, Halyun Jeong, Yifan Sun, Michael P. Friedlander:
Polar Alignment and Atomic Decomposition. CoRR abs/1912.05068 (2019) - 2018
- [i10]Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, Pooya Ronagh:
Smooth Structured Prediction Using Quantum and Classical Gibbs Samplers. CoRR abs/1809.04091 (2018) - 2016
- [i9]Aleksandr Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy, Michael P. Friedlander, Scott Roy:
Level-set methods for convex optimization. CoRR abs/1602.01506 (2016) - [i8]Michael P. Friedlander, Gabriel Goh:
Efficient evaluation of scaled proximal operators. CoRR abs/1603.05719 (2016) - [i7]Andrew Cotter, Michael P. Friedlander, Gabriel Goh, Maya R. Gupta:
Satisfying Real-world Goals with Dataset Constraints. CoRR abs/1606.07558 (2016) - 2015
- [i6]Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke:
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. CoRR abs/1506.00552 (2015) - [i5]Michael P. Friedlander, Ives Macêdo:
Low-rank spectral optimization. CoRR abs/1508.00315 (2015) - 2011
- [i4]Michael P. Friedlander, Mark Schmidt:
Hybrid Deterministic-Stochastic Methods for Data Fitting. CoRR abs/1104.2373 (2011) - [i3]Aleksandr Y. Aravkin, Michael P. Friedlander, Tristan van Leeuwen:
Robust inversion via semistochastic dimensionality reduction. CoRR abs/1110.0895 (2011) - 2010
- [i2]Michael P. Friedlander, Hassan Mansour, Rayan Saab, Özgür Yilmaz:
Recovering Compressively Sampled Signals Using Partial Support Information. CoRR abs/1010.4612 (2010) - 2009
- [i1]Ewout van den Berg, Michael P. Friedlander:
Joint-sparse recovery from multiple measurements. CoRR abs/0904.2051 (2009)
Coauthor Index
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