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ORCIDMert Gürbüzbalaban
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2020 – today
- 2024
[j22]Xuan Zhang, Necdet Serhat Aybat
, Mert Gürbüzbalaban:
Robust Accelerated Primal-Dual Methods for Computing Saddle Points. SIAM J. Optim. 34(1): 1097-1130 (2024)- [i32]Umut Simsekli, Mert Gürbüzbalaban, Sinan Yildirim, Lingjiong Zhu:
Differential Privacy of Noisy (S)GD under Heavy-Tailed Perturbations. CoRR abs/2403.02051 (2024) - 2023
- [j21]Rishabh Dixit, Mert Gürbüzbalaban, Waheed U. Bajwa:
Boundary Conditions for Linear Exit Time Gradient Trajectories Around Saddle Points: Analysis and Algorithm. IEEE Trans. Inf. Theory 69(4): 2556-2602 (2023) - [j20]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu:
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize. Trans. Mach. Learn. Res. 2023 (2023) - [c26]Anant Raj, Melih Barsbey, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. ALT 2023: 1292-1342 - [c25]Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. ICML 2023: 28578-28597 - [c24]Lingjiong Zhu, Mert Gürbüzbalaban, Anant Raj, Umut Simsekli:
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent. NeurIPS 2023 - [i31]Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. CoRR abs/2301.11885 (2023) - [i30]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu:
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD. CoRR abs/2302.05516 (2023) - [i29]Lingjiong Zhu, Mert Gürbüzbalaban, Anant Raj, Umut Simsekli:
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent. CoRR abs/2305.12056 (2023) - [i28]Rishabh Dixit, Mert Gürbüzbalaban, Waheed U. Bajwa:
Accelerated gradient methods for nonconvex optimization: Escape trajectories from strict saddle points and convergence to local minima. CoRR abs/2307.07030 (2023) - 2022
- [j19]Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Nonconvex Stochastic Optimization: Nonasymptotic Performance Bounds and Momentum-Based Acceleration. Oper. Res. 70(5): 2931-2947 (2022) - [j18]Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar, Umut Simsekli, Lingjiong Zhu:
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks. J. Mach. Learn. Res. 23: 220:1-220:96 (2022) - [j17]Mert Gürbüzbalaban, Andrzej Ruszczynski, Landi Zhu:
A Stochastic Subgradient Method for Distributionally Robust Non-convex and Non-smooth Learning. J. Optim. Theory Appl. 194(3): 1014-1041 (2022) - [j16]Nurdan Kuru, S. Ilker Birbil, Mert Gürbüzbalaban, Sinan Yildirim:
Differentially Private Accelerated Optimization Algorithms. SIAM J. Optim. 32(2): 795-821 (2022) - [j15]Bugra Can, Saeed Soori, Necdet Serhat Aybat, Maryam Mehri Dehnavi, Mert Gürbüzbalaban:
Randomized Gossiping With Effective Resistance Weights: Performance Guarantees and Applications. IEEE Trans. Control. Netw. Syst. 9(2): 524-536 (2022) - [c23]Xuan Zhang, Necdet Serhat Aybat, Mert Gürbüzbalaban:
SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems. NeurIPS 2022 - [c22]Baorun Mu, Saeed Soori, Bugra Can, Mert Gürbüzbalaban, Maryam Mehri Dehnavi:
HyLo: A Hybrid Low-Rank Natural Gradient Descent Method. SC 2022: 47:1-47:16 - [i27]Bugra Can, Mert Gürbüzbalaban, Necdet Serhat Aybat:
A Variance-Reduced Stochastic Accelerated Primal Dual Algorithm. CoRR abs/2202.09688 (2022) - [i26]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Kun Yuan, Lingjiong Zhu:
Heavy-Tail Phenomenon in Decentralized SGD. CoRR abs/2205.06689 (2022) - [i25]Anant Raj, Melih Barsbey, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. CoRR abs/2206.01274 (2022) - [i24]Mert Gürbüzbalaban, Yuanhan Hu, Lingjiong Zhu:
Penalized Langevin and Hamiltonian Monte Carlo Algorithms for Constrained Sampling. CoRR abs/2212.00570 (2022) - 2021
- [j14]Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu:
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo. J. Mach. Learn. Res. 22: 239:1-239:69 (2021) - [j13]Mert Gürbüzbalaban, Asuman E. Ozdaglar, Pablo A. Parrilo:
Why random reshuffling beats stochastic gradient descent. Math. Program. 186(1): 49-84 (2021) - [c21]Mert Gürbüzbalaban, Yuanhan Hu:
Fractional moment-preserving initialization schemes for training deep neural networks. AISTATS 2021: 2233-2241 - [c20]Bugra Can, Saeed Soori, Maryam Mehri Dehnavi, Mert Gürbüzbalaban:
L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method. CDC 2021: 2386-2393 - [c19]Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. ICML 2021: 1249-1260 - [c18]Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
The Heavy-Tail Phenomenon in SGD. ICML 2021: 3964-3975 - [c17]Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. NeurIPS 2021: 18774-18788 - [c16]Hongjian Wang, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A. Erdogdu:
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance. NeurIPS 2021: 18866-18877 - [i23]Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. CoRR abs/2102.07006 (2021) - [i22]Saeed Soori, Bugra Can, Baourun Mu, Mert Gürbüzbalaban, Maryam Mehri Dehnavi:
TENGraD: Time-Efficient Natural Gradient Descent with Exact Fisher-Block Inversion. CoRR abs/2106.03947 (2021) - [i21]Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. CoRR abs/2106.04881 (2021) - [i20]Bugra Can, Saeed Soori, Maryam Mehri Dehnavi, Mert Gürbüzbalaban:
L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method. CoRR abs/2108.09365 (2021) - 2020
- [j12]Mert Gürbüzbalaban, Asuman E. Ozdaglar, Nuri Denizcan Vanli, Stephen J. Wright:
Randomness and permutations in coordinate descent methods. Math. Program. 181(2): 349-376 (2020) - [j11]Necdet Serhat Aybat, Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar:
Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions. SIAM J. Optim. 30(1): 717-751 (2020) - [c15]Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gürbüzbalaban:
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. AISTATS 2020: 1965-1976 - [c14]Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban:
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. ICML 2020: 8970-8980 - [c13]Saeed Soori, Bugra Can, Mert Gürbüzbalaban, Maryam Mehri Dehnavi:
ASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning. IPDPS 2020: 429-439 - [c12]Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. NeurIPS 2020 - [c11]Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization. NeurIPS 2020 - [i19]Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban:
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. CoRR abs/2002.05685 (2020) - [i18]Mert Gürbüzbalaban, Yuanhan Hu:
Fractional moment-preserving initialization schemes for training fully-connected neural networks. CoRR abs/2005.11878 (2020) - [i17]Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
The Heavy-Tail Phenomenon in SGD. CoRR abs/2006.04740 (2020) - [i16]Mert Gürbüzbalaban, Andrzej Ruszczynski, Landi Zhu:
A Stochastic Subgradient Method for Distributionally Robust Non-Convex Learning. CoRR abs/2006.04873 (2020) - [i15]Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. CoRR abs/2006.06733 (2020) - [i14]Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu:
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo. CoRR abs/2007.00590 (2020) - [i13]Nurdan Kuru, S. Ilker Birbil, Mert Gürbüzbalaban, Sinan Yildirim:
Differentially Private Accelerated Optimization Algorithms. CoRR abs/2008.01989 (2020)
2010 – 2019
- 2019
- [j10]Mert Gürbüzbalaban, Asuman E. Ozdaglar, Pablo A. Parrilo:
Convergence Rate of Incremental Gradient and Incremental Newton Methods. SIAM J. Optim. 29(4): 2542-2565 (2019) - [c10]Bugra Can, Mert Gürbüzbalaban, Lingjiong Zhu:
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. ICML 2019: 891-901 - [c9]Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban:
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. ICML 2019: 5827-5837 - [c8]Thanh Huy Nguyen, Umut Simsekli, Mert Gürbüzbalaban, Gaël Richard:
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. NeurIPS 2019: 273-283 - [c7]Necdet Serhat Aybat, Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar:
A Universally Optimal Multistage Accelerated Stochastic Gradient Method. NeurIPS 2019: 8523-8534 - [i12]Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban:
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. CoRR abs/1901.06053 (2019) - [i11]Bugra Can, Mert Gürbüzbalaban, Lingjiong Zhu:
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. CoRR abs/1901.07445 (2019) - [i10]Necdet Serhat Aybat, Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar:
A Universally Optimal Multistage Accelerated Stochastic Gradient Method. CoRR abs/1901.08022 (2019) - [i9]Thanh Huy Nguyen, Umut Simsekli, Mert Gürbüzbalaban, Gaël Richard:
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. CoRR abs/1906.09069 (2019) - [i8]Saeed Soori, Bugra Can, Mert Gürbüzbalaban, Maryam Mehri Dehnavi:
ASYNC: Asynchronous Machine Learning on Distributed Systems. CoRR abs/1907.08526 (2019) - [i7]Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar, Umut Simsekli, Lingjiong Zhu:
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks. CoRR abs/1910.08701 (2019) - [i6]Umut Simsekli, Mert Gürbüzbalaban, Thanh Huy Nguyen, Gaël Richard, Levent Sagun:
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks. CoRR abs/1912.00018 (2019) - 2018
- [j9]N. Denizcan Vanli, Mert Gürbüzbalaban, Asuman E. Ozdaglar:
Global Convergence Rate of Proximal Incremental Aggregated Gradient Methods. SIAM J. Optim. 28(2): 1282-1300 (2018) - [j8]Aryan Mokhtari, Mert Gürbüzbalaban, Alejandro Ribeiro:
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate. SIAM J. Optim. 28(2): 1420-1447 (2018) - [c6]Saeed Soori, Aditya Devarakonda, Zachary Blanco, James Demmel, Mert Gürbüzbalaban, Maryam Mehri Dehnavi:
Reducing Communication in Proximal Newton Methods for Sparse Least Squares Problems. ICPP 2018: 22:1-22:10 - [i5]Necdet Serhat Aybat, Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar:
Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions. CoRR abs/1805.10579 (2018) - [i4]Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration. CoRR abs/1809.04618 (2018) - [i3]Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization. CoRR abs/1812.07725 (2018) - 2017
- [j7]Julia Eaton, Sara Grundel, Mert Gürbüzbalaban, Michael L. Overton:
Polynomial root radius optimization with affine constraints. Math. Program. 165(2): 509-528 (2017) - [j6]Mert Gürbüzbalaban, Asuman E. Ozdaglar, Pablo A. Parrilo:
On the Convergence Rate of Incremental Aggregated Gradient Algorithms. SIAM J. Optim. 27(2): 1035-1048 (2017) - [j5]Nicola Guglielmi, Mert Gürbüzbalaban, Tim Mitchell, Michael L. Overton:
Approximating the Real Structured Stability Radius with Frobenius-Norm Bounded Perturbations. SIAM J. Matrix Anal. Appl. 38(4): 1323-1353 (2017) - [c5]Necdet Serhat Aybat, Mert Gürbüzbalaban:
Decentralized computation of effective resistances and acceleration of consensus algorithms. GlobalSIP 2017: 538-542 - [c4]Aryan Mokhtari, Mert Gürbüzbalaban, Alejandro Ribeiro:
A double incremental aggregated gradient method with linear convergence rate for large-scale optimization. ICASSP 2017: 4696-4700 - [c3]Mert Gürbüzbalaban, Asuman E. Ozdaglar, Pablo A. Parrilo, Nuri Denizcan Vanli:
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent. NIPS 2017: 6999-7007 - [i2]Saeed Soori, Aditya Devarakonda, James Demmel, Mert Gürbüzbalaban, Maryam Mehri Dehnavi:
Avoiding Communication in Proximal Methods for Convex Optimization Problems. CoRR abs/1710.08883 (2017) - 2016
- [c2]N. Denizcan Vanli, Mert Gürbüzbalaban, Asuman E. Ozdaglar:
Global convergence rate of incremental aggregated gradient methods for nonsmooth problems. CDC 2016: 173-178 - [i1]Aryan Mokhtari, Mert Gürbüzbalaban, Alejandro Ribeiro:
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate. CoRR abs/1611.00347 (2016) - 2015
- [j4]Mert Gürbüzbalaban, Asuman E. Ozdaglar, Pablo A. Parrilo:
A globally convergent incremental Newton method. Math. Program. 151(1): 283-313 (2015) - 2013
- [j3]Nicola Guglielmi, Mert Gürbüzbalaban, Michael L. Overton:
Fast Approximation of the HINFINITY Norm via Optimization over Spectral Value Sets. SIAM J. Matrix Anal. Appl. 34(2): 709-737 (2013) - 2012
- [j2]Mert Gürbüzbalaban, Michael L. Overton:
Some Regularity Results for the Pseudospectral Abscissa and Pseudospectral Radius of a Matrix. SIAM J. Optim. 22(2): 281-285 (2012) - [j1]Vincent D. Blondel, Mert Gürbüzbalaban, Alexandre Megretski, Michael L. Overton:
Explicit Solutions for Root Optimization of a Polynomial Family With One Affine Constraint. IEEE Trans. Autom. Control. 57(12): 3078-3089 (2012) - 2010
- [c1]Vincent D. Blondel, Mert Gürbüzbalaban, Alexandre Megretski, Michael L. Overton:
Explicit solutions for root optimization of a polynomial family. CDC 2010: 485-488
Coauthor Index
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last updated on 2024-10-07 21:17 CEST by the dblp team
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