Brian Bullins

According to our database, Brian Bullins authored at least 30 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Convex optimization with <i>p</i>-norm oracles.

Deeksha Adil, Brian Bullins, Arun Jambulapati, Aaron Sidford

CoRR, 2024

Faster Acceleration for Steepest Descent.

Site Bai, Brian Bullins

CoRR, 2024

Local Composite Saddle Point Optimization.

Site Bai, Brian Bullins

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Federated Composite Saddle Point Optimization.

Site Bai, Brian Bullins

CoRR, 2023

Beyond first-order methods for non-convex non-concave min-max optimization.

Abhijeet Vyas, Brian Bullins

CoRR, 2023

Competitive Gradient Optimization.

Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli

Proceedings of the International Conference on Machine Learning, 2023

Variance-Reduced Conservative Policy Iteration.

Naman Agarwal, Brian Bullins, Karan Singh

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

Higher-Order Methods for Convex-Concave Min-Max Optimization and Monotone Variational Inequalities.

Brian Bullins, Kevin A. Lai

SIAM J. Optim., September, 2022

Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities.

Deeksha Adil, Brian Bullins, Arun Jambulapati, Sushant Sachdeva

CoRR, 2022

Towards Optimal Communication Complexity in Distributed Non-Convex Optimization.

Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract).

Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021

Adaptive regularization with cubics on manifolds.

Naman Agarwal, Nicolas Boumal, Brian Bullins, Coralia Cartis

Math. Program., 2021

A Stochastic Newton Algorithm for Distributed Convex Optimization.

Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization.

Deeksha Adil, Brian Bullins, Sushant Sachdeva

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Almost-Linear-Time Weighted 𝓁_p-Norm Solvers in Slightly Dense Graphs via Sparsification.

Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva

Proceedings of the 48th International Colloquium on Automata, Languages, and Programming, 2021

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication.

Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2021

2020

Is Local SGD Better than Minibatch SGD?

Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro

Proceedings of the 37th International Conference on Machine Learning, 2020

Highly smooth minimization of non-smooth problems.

Brian Bullins

Proceedings of the Conference on Learning Theory, 2020

2019

Efficient Higher-Order Optimization for Machine Learning

Brian Bullins

PhD thesis, 2019

Higher-Order Accelerated Methods for Faster Non-Smooth Optimization.

Brian Bullins, Richard Peng

CoRR, 2019

Online Control with Adversarial Disturbances.

Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh

Proceedings of the 36th International Conference on Machine Learning, 2019

Efficient Full-Matrix Adaptive Regularization.

Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang

Proceedings of the 36th International Conference on Machine Learning, 2019

Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning.

Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni

Proceedings of the Algorithmic Learning Theory, 2019

2018

The Case for Full-Matrix Adaptive Regularization.

Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang

CoRR, 2018

Not-So-Random Features.

Brian Bullins, Cyril Zhang, Yi Zhang

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Second-Order Stochastic Optimization for Machine Learning in Linear Time.

Naman Agarwal, Brian Bullins, Elad Hazan

J. Mach. Learn. Res., 2017

Finding approximate local minima faster than gradient descent.

Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma

Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017

2016

Finding Approximate Local Minima for Nonconvex Optimization in Linear Time.

Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma

CoRR, 2016

Second Order Stochastic Optimization in Linear Time.

Naman Agarwal, Brian Bullins, Elad Hazan

CoRR, 2016

The Limits of Learning with Missing Data.

Brian Bullins, Elad Hazan, Tomer Koren

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016