Seuraa
Mert Pilanci
Mert Pilanci
Stanford University
Vahvistettu sähköpostiosoite verkkotunnuksessa stanford.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
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
2902017
Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares
M Pilanci, MJ Wainwright
The Journal of Machine Learning Research 17 (1), 1842-1879, 2016
2292016
Randomized sketches of convex programs with sharp guarantees
M Pilanci, MJ Wainwright
IEEE Transactions on Information Theory 61 (9), 5096-5115, 2015
1902015
Randomized sketches for kernels: Fast and optimal nonparametric regression
Y Yang, M Pilanci, MJ Wainwright
1672017
Sparse learning via Boolean relaxations
M Pilanci, MJ Wainwright, L El Ghaoui
Mathematical Programming 151 (1), 63-87, 2015
792015
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
782020
Recovery of sparse probability measures via convex programming
M Pilanci, L Ghaoui, V Chandrasekaran
Advances in Neural Information Processing Systems 25, 2012
632012
Revealing the structure of deep neural networks via convex duality
T Ergen, M Pilanci
International Conference on Machine Learning, 3004-3014, 2021
602021
Convex geometry and duality of over-parameterized neural networks
T Ergen, M Pilanci
The Journal of Machine Learning Research 22 (1), 9646-9708, 2021
452021
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
372020
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
362020
Randomized sketches for kernels: Fast and optimal non-parametric regression
Y Yang, M Pilanci, MJ Wainwright
arXiv preprint arXiv:1501.06195, 2015
342015
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
292020
Structured least squares problems and robust estimators
M Pilanci, O Arikan, MC Pinar
IEEE transactions on signal processing 58 (5), 2453-2465, 2010
292010
Optimal randomized first-order methods for least-squares problems
J Lacotte, M Pilanci
International Conference on Machine Learning, 5587-5597, 2020
282020
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
282020
Global optimality beyond two layers: Training deep relu networks via convex programs
T Ergen, M Pilanci
International Conference on Machine Learning, 2993-3003, 2021
272021
Convex regularization behind neural reconstruction
A Sahiner, M Mardani, B Ozturkler, M Pilanci, J Pauly
arXiv preprint arXiv:2012.05169, 2020
272020
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
252021
Faster least squares optimization
J Lacotte, M Pilanci
arXiv preprint arXiv:1911.02675, 2019
242019
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Artikkelit 1–20