Brian Bullins

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 36

Top Topics

Convex Optimization

Top Venues

Publications

Site Bai, Brian Bullins
Local Composite Saddle Point Optimization. ICLR (2024)
Naman Agarwal, Brian Bullins, Karan Singh
Variance-Reduced Conservative Policy Iteration. ALT (2023)
Site Bai, Brian Bullins
Federated Composite Saddle Point Optimization. CoRR (2023)
Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli
Competitive Gradient Optimization. ICML (2023)
Abhijeet Vyas, Brian Bullins
Beyond first-order methods for non-convex non-concave min-max optimization. CoRR (2023)
Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. NeurIPS (2022)
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)
Brian Bullins, Kevin A. Lai
Higher-Order Methods for Convex-Concave Min-Max Optimization and Monotone Variational Inequalities. SIAM J. Optim. 32 (3) (2022)
Deeksha Adil, Brian Bullins, Arun Jambulapati, Sushant Sachdeva
Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities. CoRR (2022)
Naman Agarwal, Brian Bullins, Karan Singh
Variance-Reduced Conservative Policy Iteration. CoRR (2022)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR (2021)
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS (2021)
Deeksha Adil, Brian Bullins, Sushant Sachdeva
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. CoRR (2021)
Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva
-norm Solvers in Slightly Dense Graphs via Sparsification. CoRR (2021)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT (2021)
Deeksha Adil, Brian Bullins, Sushant Sachdeva
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. NeurIPS (2021)
Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva
-Norm Solvers in Slightly Dense Graphs via Sparsification. ICALP (2021)
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR (2021)
Naman Agarwal, Nicolas Boumal, Brian Bullins, Coralia Cartis
Adaptive regularization with cubics on manifolds. Math. Program. 188 (1) (2021)
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)
Brian Bullins
Highly smooth minimization of non-smooth problems. COLT (2020)
Brian Bullins, Kevin A. Lai
Higher-order methods for convex-concave min-max optimization and monotone variational inequalities. CoRR (2020)
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 (2020)
Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni
Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning. ALT (2019)
Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh
Online Control with Adversarial Disturbances. CoRR (2019)
Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang
Efficient Full-Matrix Adaptive Regularization. ICML (2019)
Brian Bullins, Richard Peng
Higher-Order Accelerated Methods for Faster Non-Smooth Optimization. CoRR (2019)
Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh
Online Control with Adversarial Disturbances. ICML (2019)
Brian Bullins, Cyril Zhang, Yi Zhang
Not-So-Random Features. ICLR (Poster) (2018)
Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang
The Case for Full-Matrix Adaptive Regularization. CoRR (2018)
Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma
Finding approximate local minima faster than gradient descent. STOC (2017)
Naman Agarwal, Brian Bullins, Elad Hazan
Second-Order Stochastic Optimization for Machine Learning in Linear Time. J. Mach. Learn. Res. 18 (2017)
Brian Bullins, Cyril Zhang, Yi Zhang
Not-So-Random Features. CoRR (2017)
Naman Agarwal, Brian Bullins, Elad Hazan
Second Order Stochastic Optimization in Linear Time. CoRR (2016)
Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma
Finding Approximate Local Minima for Nonconvex Optimization in Linear Time. CoRR (2016)
Brian Bullins, Elad Hazan, Tomer Koren
The Limits of Learning with Missing Data. NIPS (2016)