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Mert Pilanci
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Newton sketch: A near linear-time optimization algorithm with linear-quadratic convergence
M Pilanci, MJ Wainwright
SIAM Journal on Optimization 27 (1), 205-245, 2017
3242017
Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares
M Pilanci, MJ Wainwright
Journal of Machine Learning Research 17 (53), 1-38, 2016
2442016
Randomized sketches of convex programs with sharp guarantees
M Pilanci, MJ Wainwright
IEEE Transactions on Information Theory 61 (9), 5096-5115, 2015
2012015
Randomized sketches for kernels: Fast and optimal nonparametric regression
Y Yang, M Pilanci, MJ Wainwright
1922017
Neural networks are convex regularizers: Exact polynomial-time convex optimization formulations for two-layer networks
M Pilanci, T Ergen
International Conference on Machine Learning, 7695-7705, 2020
1042020
Sparse learning via Boolean relaxations
M Pilanci, MJ Wainwright, L El Ghaoui
Mathematical Programming 151 (1), 63-87, 2015
862015
Revealing the structure of deep neural networks via convex duality
T Ergen, M Pilanci
International Conference on Machine Learning, 3004-3014, 2021
722021
Recovery of sparse probability measures via convex programming
M Pilanci, L Ghaoui, V Chandrasekaran
Advances in Neural Information Processing Systems 25, 2012
652012
Convex geometry and duality of over-parameterized neural networks
T Ergen, M Pilanci
Journal of machine learning research 22 (212), 1-63, 2021
562021
Implicit convex regularizers of cnn architectures: Convex optimization of two-and three-layer networks in polynomial time
T Ergen, M Pilanci
arXiv preprint arXiv:2006.14798, 2020
452020
Vector-output relu neural network problems are copositive programs: Convex analysis of two layer networks and polynomial-time algorithms
A Sahiner, T Ergen, J Pauly, M Pilanci
arXiv preprint arXiv:2012.13329, 2020
402020
Randomized sketches for kernels: Fast and optimal non-parametric regression
Y Yang, M Pilanci, MJ Wainwright
arXiv preprint arXiv:1501.06195, 2015
362015
Global optimality beyond two layers: Training deep relu networks via convex programs
T Ergen, M Pilanci
International Conference on Machine Learning, 2993-3003, 2021
352021
Optimal randomized first-order methods for least-squares problems
J Lacotte, M Pilanci
International Conference on Machine Learning, 5587-5597, 2020
322020
Demystifying batch normalization in relu networks: Equivalent convex optimization models and implicit regularization
T Ergen, A Sahiner, B Ozturkler, J Pauly, M Mardani, M Pilanci
arXiv preprint arXiv:2103.01499, 2021
312021
Convex geometry of two-layer relu networks: Implicit autoencoding and interpretable models
T Ergen, M Pilanci
International Conference on Artificial Intelligence and Statistics, 4024-4033, 2020
312020
Newton-LESS: Sparsification without trade-offs for the sketched Newton update
M Derezinski, J Lacotte, M Pilanci, MW Mahoney
Advances in Neural Information Processing Systems 34, 2835-2847, 2021
302021
Debiasing distributed second order optimization with surrogate sketching and scaled regularization
M Derezinski, B Bartan, M Pilanci, MW Mahoney
Advances in Neural Information Processing Systems 33, 6684-6695, 2020
302020
Structured least squares problems and robust estimators
M Pilanci, O Arikan, MC Pinar
IEEE transactions on signal processing 58 (5), 2453-2465, 2010
292010
The hidden convex optimization landscape of regularized two-layer relu networks: an exact characterization of optimal solutions
Y Wang, J Lacotte, M Pilanci
International Conference on Learning Representations, 2021
282021
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Artikelen 1–20