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ORCIDBrian Bullins
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2020 – today
- 2024
[c17]Site Bai, Brian Bullins:
Local Composite Saddle Point Optimization. ICLR 2024- [i17]Site Bai, Brian Bullins:
Faster Acceleration for Steepest Descent. CoRR abs/2409.19200 (2024) - 2023
- [c16]Naman Agarwal, Brian Bullins, Karan Singh:
Variance-Reduced Conservative Policy Iteration. ALT 2023: 3-33 - [c15]Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli:
Competitive Gradient Optimization. ICML 2023: 35243-35276 - [i16]Abhijeet Vyas, Brian Bullins:
Beyond first-order methods for non-convex non-concave min-max optimization. CoRR abs/2304.08389 (2023) - [i15]Site Bai, Brian Bullins:
Federated Composite Saddle Point Optimization. CoRR abs/2305.15643 (2023) - 2022
- [j3]Brian Bullins, Kevin A. Lai:
Higher-Order Methods for Convex-Concave Min-Max Optimization and Monotone Variational Inequalities. SIAM J. Optim. 32(3): 2208-2229 (2022) - [c14]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). IJCAI 2022: 5359-5363 - [c13]Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro:
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. NeurIPS 2022 - [i14]Deeksha Adil, Brian Bullins, Arun Jambulapati, Sushant Sachdeva:
Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities. CoRR abs/2205.06167 (2022) - [i13]Naman Agarwal, Brian Bullins, Karan Singh:
Variance-Reduced Conservative Policy Iteration. CoRR abs/2212.06283 (2022) - 2021
- [j2]Naman Agarwal, Nicolas Boumal
, Brian Bullins, Coralia Cartis:
Adaptive regularization with cubics on manifolds. Math. Program. 188(1): 85-134 (2021) - [c12]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT 2021: 4386-4437 - [c11]Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva:
Almost-Linear-Time Weighted 𝓁p-Norm Solvers in Slightly Dense Graphs via Sparsification. ICALP 2021: 9:1-9:15 - [c10]Deeksha Adil, Brian Bullins, Sushant Sachdeva:
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. NeurIPS 2021: 19122-19133 - [c9]Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth:
A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS 2021: 26818-26830 - [i12]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR abs/2102.01583 (2021) - [i11]Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva:
Almost-linear-time Weighted 𝓁p-norm Solvers in Slightly Dense Graphs via Sparsification. CoRR abs/2102.06977 (2021) - [i10]Deeksha Adil, Brian Bullins, Sushant Sachdeva:
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. CoRR abs/2107.02432 (2021) - [i9]Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth:
A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR abs/2110.02954 (2021) - 2020
- [c8]Brian Bullins:
Highly smooth minimization of non-smooth problems. COLT 2020: 988-1030 - [c7]Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro:
Is Local SGD Better than Minibatch SGD? ICML 2020: 10334-10343 - [i8]Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro:
Is Local SGD Better than Minibatch SGD? CoRR abs/2002.07839 (2020) - [i7]Brian Bullins, Kevin A. Lai:
Higher-order methods for convex-concave min-max optimization and monotone variational inequalities. CoRR abs/2007.04528 (2020)
2010 – 2019
- 2019
- [b1]Brian Bullins:
Efficient Higher-Order Optimization for Machine Learning. Princeton University, USA, 2019 - [c6]Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni:
Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning. ALT 2019: 235-246 - [c5]Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang:
Efficient Full-Matrix Adaptive Regularization. ICML 2019: 102-110 - [c4]Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh:
Online Control with Adversarial Disturbances. ICML 2019: 111-119 - [i6]Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh:
Online Control with Adversarial Disturbances. CoRR abs/1902.08721 (2019) - [i5]Brian Bullins, Richard Peng:
Higher-Order Accelerated Methods for Faster Non-Smooth Optimization. CoRR abs/1906.01621 (2019) - 2018
- [c3]Brian Bullins, Cyril Zhang, Yi Zhang:
Not-So-Random Features. ICLR (Poster) 2018 - [i4]Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang:
The Case for Full-Matrix Adaptive Regularization. CoRR abs/1806.02958 (2018) - 2017
- [j1]Naman Agarwal, Brian Bullins, Elad Hazan:
Second-Order Stochastic Optimization for Machine Learning in Linear Time. J. Mach. Learn. Res. 18: 116:1-116:40 (2017) - [c2]Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma:
Finding approximate local minima faster than gradient descent. STOC 2017: 1195-1199 - [i3]Brian Bullins, Cyril Zhang, Yi Zhang:
Not-So-Random Features. CoRR abs/1710.10230 (2017) - 2016
- [c1]Brian Bullins, Elad Hazan, Tomer Koren:
The Limits of Learning with Missing Data. NIPS 2016: 3495-3503 - [i2]Naman Agarwal, Brian Bullins, Elad Hazan:
Second Order Stochastic Optimization in Linear Time. CoRR abs/1602.03943 (2016) - [i1]Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma:
Finding Approximate Local Minima for Nonconvex Optimization in Linear Time. CoRR abs/1611.01146 (2016)
Coauthor Index
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last updated on 2024-10-22 21:15 CEST by the dblp team
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