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Tolga ErgenResearch Scientist, LG AI ResearchEmail yang diverifikasi di stanford.edu
Jonathan LacotteStanford UniversityEmail yang diverifikasi di stanford.edu
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyEmail yang diverifikasi di mit.edu
Burak BartanResearcher, Qualcomm AI ResearchEmail yang diverifikasi di stanford.edu
Orhan ArikanBilkent UniversityEmail yang diverifikasi di ee.bilkent.edu.tr
Arda SahinerStanford UniversityEmail yang diverifikasi di stanford.edu
John PaulyProfessor of Electrical Engineering, Stanford UniversityEmail yang diverifikasi di stanford.edu
Yifei Wang 汪祎非Stanford UniversityEmail yang diverifikasi di stanford.edu
Alfred HeroJohn H Holland Distinguished University Professor, University of MichiganEmail yang diverifikasi di eecs.umich.edu
Neophytos CharalambidesUniversity of California, San DiegoEmail yang diverifikasi di ucsd.edu
Shreyas VasanawalaProfessor of Radiology, Stanford UniversityEmail yang diverifikasi di stanford.edu
Rajarshi SahaPh.D. candidate, Stanford UniversityEmail yang diverifikasi di stanford.edu
Aaron MishkinPhD Student, Stanford UniversityEmail yang diverifikasi di cs.stanford.edu
Yun YangDepartment of Mathematics, University of Maryland, College ParkEmail yang diverifikasi di umd.edu
Ibrahim Kurban OzaslanPh.D. Student, USCEmail yang diverifikasi di usc.edu
Laurent El GhaouiVinUniversityEmail yang diverifikasi di vinuni.edu.vn
Michael MahoneyProfessor of Statistics, UC BerkeleyEmail yang diverifikasi di stat.berkeley.edu
Mustafa Çelebi PinarProfessor of Industrial Eng. Bilkent UniversityEmail yang diverifikasi di bilkent.edu.tr
Michał DerezińskiAssistant Professor at University of MichiganEmail yang diverifikasi di umich.edu
Christopher SandinoResearch Engineer, AppleEmail yang diverifikasi di alumni.stanford.edu

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Mert Pilanci

Stanford University

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Machine Learning Optimization Neural Networks Signal Processing Information Theory


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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	350	2017
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	251	2016
Randomized sketches of convex programs with sharp guarantees M Pilanci, MJ Wainwright IEEE Transactions on Information Theory 61 (9), 5096-5115, 2015	210	2015
Randomized sketches for kernels: Fast and optimal nonparametric regression Y Yang, M Pilanci, MJ Wainwright	206	2017
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	120	2020
Sparse learning via Boolean relaxations M Pilanci, MJ Wainwright, L El Ghaoui Mathematical Programming 151 (1), 63-87, 2015	95	2015
Revealing the structure of deep neural networks via convex duality T Ergen, M Pilanci International Conference on Machine Learning, 3004-3014, 2021	75	2021
Convex geometry and duality of over-parameterized neural networks T Ergen, M Pilanci Journal of machine learning research 22 (212), 1-63, 2021	65	2021
Recovery of sparse probability measures via convex programming M Pilanci, L Ghaoui, V Chandrasekaran Advances in Neural Information Processing Systems 25, 2012	64	2012
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	54	2020
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	47	2020
Global optimality beyond two layers: Training deep relu networks via convex programs T Ergen, M Pilanci International Conference on Machine Learning, 2993-3003, 2021	45	2021
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	38	2021
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	37	2021
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	37	2021
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	36	2020
Randomized sketches for kernels: Fast and optimal non-parametric regression Y Yang, M Pilanci, MJ Wainwright arXiv preprint arXiv:1501.06195, 2015	36	2015
Fast convex optimization for two-layer relu networks: Equivalent model classes and cone decompositions A Mishkin, A Sahiner, M Pilanci International Conference on Machine Learning, 15770-15816, 2022	34	2022
Unraveling attention via convex duality: Analysis and interpretations of vision transformers A Sahiner, T Ergen, B Ozturkler, J Pauly, M Mardani, M Pilanci International Conference on Machine Learning, 19050-19088, 2022	33	2022
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	33	2020

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