Brian Bullins - RankMe

48 papers A* 17B 2Journal 26Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Decoupling Variance and Scale-Invariant Updates in Adaptive Gradient Descent for Unified Vector and Matrix Optimization. CoRR Zitao Song, Cedar Site Bai, Zhe Zhang, Brian Bullins, David F. Gleich
2026	J	`jnl`	Online Min-Max Optimization: From Individual Regrets to Cumulative Saddle Points. CoRR Abhijeet Vyas, Brian Bullins
2025	J	`jnl`	Balancing Gradient and Hessian Queries in Non-Convex Optimization. CoRR Deeksha Adil, Brian Bullins, Aaron Sidford, Chenyi Zhang
2025	A*	`conf`	Faster Acceleration for Steepest Descent. COLT Cedar Site Bai, Brian Bullins
2025	A*	`conf`	Model Immunization from a Condition Number Perspective. ICML Amber Yijia Zheng, Site Bai, Brian Bullins, Raymond A. Yeh
2025	J	`jnl`	Model Immunization from a Condition Number Perspective. CoRR Amber Yijia Zheng, Cedar Site Bai, Brian Bullins, Raymond A. Yeh
2025	J	`jnl`	Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓ CoRR Xinyu Luo, Cedar Site Bai, Bolian Li, Petros Drineas, Ruqi Zhang, Brian Bullins
2025	A*	`conf`	Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimization. ICML Xinyu Luo, Site Bai, Bolian Li, Petros Drineas, Ruqi Zhang, Brian Bullins
2025	A*	`conf`	Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity. ICLR Site Bai, Brian Bullins
2024	J	`jnl`	Convex optimization with CoRR Deeksha Adil, Brian Bullins, Arun Jambulapati, Aaron Sidford
2024	J	`jnl`	Faster Acceleration for Steepest Descent. CoRR Site Bai, Brian Bullins
2024	A*	`conf`	Local Composite Saddle Point Optimization. ICLR Site Bai, Brian Bullins
2023	J	`jnl`	Beyond first-order methods for non-convex non-concave min-max optimization. CoRR Abhijeet Vyas, Brian Bullins
2023	A*	`conf`	Competitive Gradient Optimization. ICML Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli
2023	J	`jnl`	Federated Composite Saddle Point Optimization. CoRR Site Bai, Brian Bullins
2023	B	`conf`	Variance-Reduced Conservative Policy Iteration. ALT Naman Agarwal, Brian Bullins, Karan Singh
2022	J	`jnl`	Higher-Order Methods for Convex-Concave Min-Max Optimization and Monotone Variational Inequalities. SIAM J. Optim. Brian Bullins, Kevin A. Lai
2022	J	`jnl`	Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities. CoRR Deeksha Adil, Brian Bullins, Arun Jambulapati, Sushant Sachdeva
2022	A*	`conf`	The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). IJCAI Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
2022	A*	`conf`	Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. NeurIPS Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro
2022	J	`jnl`	Variance-Reduced Conservative Policy Iteration. CoRR Naman Agarwal, Brian Bullins, Karan Singh
2021	A*	`conf`	A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
2021	J	`jnl`	A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
2021	J	`jnl`	Adaptive regularization with cubics on manifolds. Math. Program. Naman Agarwal, Nicolas Boumal, Brian Bullins, Coralia Cartis
2021	A*	`conf`	Almost-Linear-Time Weighted 𝓁 ICALP Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva
2021	J	`jnl`	Almost-linear-time Weighted 𝓁 CoRR Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva
2021	A*	`conf`	The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
2021	J	`jnl`	The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
2021	A*	`conf`	Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. NeurIPS Deeksha Adil, Brian Bullins, Sushant Sachdeva
2021	J	`jnl`	Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. CoRR Deeksha Adil, Brian Bullins, Sushant Sachdeva
2020	J	`jnl`	Higher-order methods for convex-concave min-max optimization and monotone variational inequalities. CoRR Brian Bullins, Kevin A. Lai
2020	A*	`conf`	Highly smooth minimization of non-smooth problems. COLT Brian Bullins
2020	A*	`conf`	Is Local SGD Better than Minibatch SGD? ICML Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
2020	J	`jnl`	Is Local SGD Better than Minibatch SGD? CoRR Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
2019	A*	`conf`	Efficient Full-Matrix Adaptive Regularization. ICML Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang
2019	—	`—`	Efficient Higher-Order Optimization for Machine Learning Brian Bullins
2019	B	`conf`	Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning. ALT Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni
2019	J	`jnl`	Higher-Order Accelerated Methods for Faster Non-Smooth Optimization. CoRR Brian Bullins, Richard Peng
2019	A*	`conf`	Online Control with Adversarial Disturbances. ICML Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh
2019	J	`jnl`	Online Control with Adversarial Disturbances. CoRR Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh
2018	—	`conf`	Not-So-Random Features. ICLR (Poster) Brian Bullins, Cyril Zhang, Yi Zhang
2018	J	`jnl`	The Case for Full-Matrix Adaptive Regularization. CoRR Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang
2017	A*	`conf`	Finding approximate local minima faster than gradient descent. STOC Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma
2017	J	`jnl`	Not-So-Random Features. CoRR Brian Bullins, Cyril Zhang, Yi Zhang
2017	J	`jnl`	Second-Order Stochastic Optimization for Machine Learning in Linear Time. J. Mach. Learn. Res. Naman Agarwal, Brian Bullins, Elad Hazan
2016	J	`jnl`	Finding Approximate Local Minima for Nonconvex Optimization in Linear Time. CoRR Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma
2016	J	`jnl`	Second Order Stochastic Optimization in Linear Time. CoRR Naman Agarwal, Brian Bullins, Elad Hazan
2016	—	`conf`	The Limits of Learning with Missing Data. NIPS Brian Bullins, Elad Hazan, Tomer Koren