Bullins, Brian

21 publications

NeurIPS 2025 Balancing Gradient and Hessian Queries in Non-Convex Optimization Deeksha Adil, Brian Bullins, Aaron Sidford, Chenyi Zhang
COLT 2025 Faster Acceleration for Steepest Descent Cedar Site Bai, Brian Bullins
ICML 2025 Model Immunization from a Condition Number Perspective Amber Yijia Zheng, Site Bai, Brian Bullins, Raymond A. Yeh
ICML 2025 Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization Xinyu Luo, Site Bai, Bolian Li, Petros Drineas, Ruqi Zhang, Brian Bullins
ICLR 2025 Tight Lower Bounds Under Asymmetric High-Order Hölder Smoothness and Uniform Convexity Site Bai, Brian Bullins
ICLR 2024 Local Composite Saddle Point Optimization Site Bai, Brian Bullins
ICML 2023 Competitive Gradient Optimization Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli
ALT 2023 Variance-Reduced Conservative Policy Iteration Naman Agarwal, Brian Bullins, Karan Singh
IJCAI 2022 The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract) Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
NeurIPS 2022 Towards Optimal Communication Complexity in Distributed Non-Convex Optimization Kumar Kshitij Patel, Lingxiao Wang, Blake E Woodworth, Brian Bullins, Nati Srebro
NeurIPS 2021 A Stochastic Newton Algorithm for Distributed Convex Optimization Brian Bullins, Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E Woodworth
COLT 2021 The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication Blake E Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
NeurIPS 2021 Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization Deeksha Adil, Brian Bullins, Sushant Sachdeva
COLT 2020 Highly Smooth Minimization of Non-Smooth Problems Brian Bullins
ICML 2020 Is Local SGD Better than Minibatch SGD? Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, Nathan Srebro
ICML 2019 Efficient Full-Matrix Adaptive Regularization Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang
ALT 2019 Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni
ICML 2019 Online Control with Adversarial Disturbances Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh
ICLR 2018 Not-so-Random Features Brian Bullins, Cyril Zhang, Yi Zhang
JMLR 2017 Second-Order Stochastic Optimization for Machine Learning in Linear Time Naman Agarwal, Brian Bullins, Elad Hazan
NeurIPS 2016 The Limits of Learning with Missing Data Brian Bullins, Elad Hazan, Tomer Koren