Michael P. Friedlander

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 39

Top Topics

Simple And Easy To Implement

Structured Prediction

Nonlinear Optimization

Geodesic Active

Top Venues

SIAM J. Sci. Comput.

Publications

Zhenan Fan, Huang Fang, Michael P. Friedlander
Cardinality-constrained structured data-fitting problems. Open J. Math. Optim. 5 (2024)
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)
Michael P. Friedlander, Ariel Goodwin, Tim Hoheisel
From Perspective Maps to Epigraphical Projections. Math. Oper. Res. 48 (3) (2023)
Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang
Fair and efficient contribution valuation for vertical federated learning. CoRR (2022)
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)
Zhenan Fan, Halyun Jeong, Babhru Joshi, Michael P. Friedlander
Polar Deconvolution of Mixed Signals. IEEE Trans. Signal Process. 70 (2022)
Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, Pooya Ronagh
Quantum algorithms for structured prediction. Quantum Mach. Intell. 4 (2) (2022)
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 (2022)
Zhenan Fan, Zirui Zhou, Jian Pei, Michael P. Friedlander, Jiajie Hu, Chengliang Li, Yong Zhang
Knowledge-Injected Federated Learning. CoRR (2022)
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) (2022)
Zhenan Fan, Huang Fang, Michael P. Friedlander
A dual approach for federated learning. CoRR (2022)
Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, Yong Zhang
Improving Fairness for Data Valuation in Federated Learning. CoRR (2021)
Huang Fang, Zhenan Fan, Michael P. Friedlander
Fast convergence of stochastic subgradient method under interpolation. ICLR (2021)
Michael P. Friedlander, Ariel Goodwin, Tim Hoheisel
From perspective maps to epigraphical projections. CoRR (2021)
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) (2020)
Zhenan Fan, Halyun Jeong, Yifan Sun, Michael P. Friedlander
Atomic Decomposition via Polar Alignment: The Geometry of Structured Optimization. Found. Trends Optim. 3 (4) (2020)
Zhenan Fan, Halyun Jeong, Babhru Joshi, Michael P. Friedlander
Polar Deconvolution of Mixed Signals. CoRR (2020)
Huang Fang, Zhenan Fan, Yifan Sun, Michael P. Friedlander
Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization. AISTATS (2020)
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) (2020)
Ron Estrin, Michael P. Friedlander
A perturbation view of level-set methods for convex optimization. Optim. Lett. 14 (8) (2020)
Huang Fang, Nick Harvey, Victor S. Portella, Michael P. Friedlander
Online mirror descent and dual averaging: keeping pace in the dynamic case. ICML (2020)
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 (2020)
Michael P. Friedlander, Halyun Jeong, Yaniv Plan, Özgür Yilmaz
NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate. CoRR (2020)
Ron Estrin, Michael P. Friedlander, Dominique Orban, Michael A. Saunders
Implementing a smooth exact penalty function for equality-constrained nonlinear optimization. CoRR (2019)
Yifan Sun, Michael P. Friedlander
One-shot atomic detection. CAMSAP (2019)
Ron Estrin, Michael P. Friedlander, Dominique Orban, Michael A. Saunders
Implementing a smooth exact penalty function for general constrained nonlinear optimization. CoRR (2019)
Michael P. Friedlander, Ives Macêdo, Ting Kei Pong
Polar Convolution. SIAM J. Optim. 29 (2) (2019)
Aleksandr Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy, Michael P. Friedlander, Scott Roy
Level-set methods for convex optimization. Math. Program. 174 (1-2) (2019)
Zhenan Fan, Halyun Jeong, Yifan Sun, Michael P. Friedlander
Polar Alignment and Atomic Decomposition. CoRR (2019)
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)
Zhenan Fan, Yifan Sun, Michael P. Friedlander
Bundle methods for dual atomic pursuit. ACSSC (2019)
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) (2018)
Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, Pooya Ronagh
Smooth Structured Prediction Using Quantum and Classical Gibbs Samplers. CoRR (2018)
Andrew Cotter, Michael P. Friedlander, Gabriel Goh, Maya R. Gupta
Satisfying Real-world Goals with Dataset Constraints. CoRR (2016)
Aleksandr Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy, Michael P. Friedlander, Scott Roy
Level-set methods for convex optimization. CoRR (2016)
Gabriel Goh, Andrew Cotter, Maya R. Gupta, Michael P. Friedlander
Satisfying Real-world Goals with Dataset Constraints. NIPS (2016)
Michael P. Friedlander, Ives Macêdo
Low-Rank Spectral Optimization via Gauge Duality. SIAM J. Sci. Comput. 38 (3) (2016)
Michael P. Friedlander, Nathan Krislock, Ting Kei Pong
Social Resistance. Comput. Sci. Eng. 18 (2) (2016)
Michael P. Friedlander, Gabriel Goh
Efficient evaluation of scaled proximal operators. CoRR (2016)
Michael P. Friedlander, Ives Macêdo
Low-rank spectral optimization. CoRR (2015)
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)
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 (2015)
Michael P. Friedlander, Ives Macêdo, Ting Kei Pong
Gauge Optimization and Duality. SIAM J. Optim. 24 (4) (2014)
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)
Aleksandr Y. Aravkin, James V. Burke, Michael P. Friedlander
Variational Properties of Value Functions. SIAM J. Optim. 23 (3) (2013)
Michael P. Friedlander, Mark Schmidt
Erratum: Hybrid Deterministic-Stochastic Methods for Data Fitting. SIAM J. Sci. Comput. 35 (4) (2013)
Aleksandr Y. Aravkin, Michael P. Friedlander, Tristan van Leeuwen
Robust inversion via semistochastic dimensionality reduction. ICASSP (2012)
Michael P. Friedlander, Hassan Mansour, Rayan Saab, Özgür Yilmaz
Recovering Compressively Sampled Signals Using Partial Support Information. IEEE Trans. Inf. Theory 58 (2) (2012)
Felix J. Herrmann, Michael P. Friedlander, Özgür Yilmaz
Fighting the Curse of Dimensionality: Compressive Sensing in Exploration Seismology. IEEE Signal Process. Mag. 29 (3) (2012)
Michael P. Friedlander, Dominique Orban
A primal-dual regularized interior-point method for convex quadratic programs. Math. Program. Comput. 4 (1) (2012)
Aleksandr Y. Aravkin, Michael P. Friedlander, Felix J. Herrmann, Tristan van Leeuwen
Robust inversion, dimensionality reduction, and randomized sampling. Math. Program. 134 (1) (2012)
Michael P. Friedlander, Mark Schmidt
Hybrid Deterministic-Stochastic Methods for Data Fitting. SIAM J. Sci. Comput. 34 (3) (2012)
Michael P. Friedlander, Mark Schmidt
Hybrid Deterministic-Stochastic Methods for Data Fitting CoRR (2011)
Aleksandr Y. Aravkin, Michael P. Friedlander, Tristan van Leeuwen
Robust inversion via semistochastic dimensionality reduction CoRR (2011)
Ewout van den Berg, Michael P. Friedlander
Sparse Optimization with Least-Squares Constraints. SIAM J. Optim. 21 (4) (2011)
Michael P. Friedlander, Hassan Mansour, Rayan Saab, Özgür Yilmaz
Recovering Compressively Sampled Signals Using Partial Support Information CoRR (2010)
Ewout van den Berg, Michael P. Friedlander
Theoretical and empirical results for recovery from multiple measurements. IEEE Trans. Inf. Theory 56 (5) (2010)
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)
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) (2009)
Ewout van den Berg, Michael P. Friedlander
Joint-sparse recovery from multiple measurements CoRR (2009)
Ewout van den Berg, Michael P. Friedlander
Probing the Pareto Frontier for Basis Pursuit Solutions. SIAM J. Sci. Comput. 31 (2) (2008)
Michael P. Friedlander, Kathrin Hatz
Computing non-negative tensor factorizations. Optim. Methods Softw. 23 (4) (2008)
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) (2008)
Michael P. Friedlander, Paul Tseng
Exact Regularization of Convex Programs. SIAM J. Optim. 18 (4) (2007)
Michael P. Friedlander, M. R. Gupta
On minimizing distortion and relative entropy. IEEE Trans. Inf. Theory 52 (1) (2006)
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) (2005)
Michael P. Friedlander, Michael A. Saunders
A Globally Convergent Linearly Constrained Lagrangian Method for Nonlinear Optimization. SIAM J. Optim. 15 (3) (2005)