The Limits of Learning with Missing Data. - Brian Bullins; Elad Hazan; Tomer Koren (NIPS, 2016)

Second Order Stochastic Optimization in Linear Time. - Naman Agarwal; Brian Bullins; Elad Hazan (CoRR, 2016)

Finding Approximate Local Minima for Nonconvex Optimization in Linear Time. - Naman Agarwal; Zeyuan Allen Zhu; Brian Bullins; Elad Hazan; Tengyu Ma 001 (CoRR, 2016)

Finding approximate local minima faster than gradient descent. - Naman Agarwal; Zeyuan Allen Zhu; Brian Bullins; Elad Hazan; Tengyu Ma 001 (STOC, 2017)

Second-Order Stochastic Optimization for Machine Learning in Linear Time. - Naman Agarwal; Brian Bullins; Elad Hazan (J. Mach. Learn. Res., 2017)

Not-So-Random Features. - Brian Bullins; Cyril Zhang; Yi Zhang (ICLR (Poster), 2018)

The Case for Full-Matrix Adaptive Regularization. - Naman Agarwal; Brian Bullins; Xinyi Chen 001; Elad Hazan; Karan Singh; Cyril Zhang; Yi Zhang 074 (CoRR, 2018)

Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning. - Brian Bullins; Elad Hazan; Adam Kalai; Roi Livni (ALT, 2019)

Efficient Full-Matrix Adaptive Regularization. - Naman Agarwal; Brian Bullins; Xinyi Chen 001; Elad Hazan; Karan Singh; Cyril Zhang; Yi Zhang 074 (ICML, 2019)

Online Control with Adversarial Disturbances. - Naman Agarwal; Brian Bullins; Elad Hazan; Sham M. Kakade; Karan Singh (ICML, 2019)

Higher-Order Accelerated Methods for Faster Non-Smooth Optimization. - Brian Bullins; Richard Peng (CoRR, 2019)

Efficient Higher-Order Optimization for Machine Learning - Brian Bullins (2019)

Highly smooth minimization of non-smooth problems. - Brian Bullins (COLT, 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 (ICML, 2020)

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. - Blake E. Woodworth; Brian Bullins; Ohad Shamir; Nathan Srebro (COLT, 2021)

Almost-Linear-Time Weighted 𝓁p-Norm Solvers in Slightly Dense Graphs via Sparsification. - Deeksha Adil; Brian Bullins; Rasmus Kyng; Sushant Sachdeva (ICALP, 2021)

Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. - Deeksha Adil; Brian Bullins; Sushant Sachdeva (NeurIPS, 2021)

A Stochastic Newton Algorithm for Distributed Convex Optimization. - Brian Bullins; Kumar Kshitij Patel; Ohad Shamir; Nathan Srebro; Blake E. Woodworth (NeurIPS, 2021)

Adaptive regularization with cubics on manifolds. - Naman Agarwal; Nicolas Boumal; Brian Bullins; Coralia Cartis (Math. Program., 2021)

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). - Blake E. Woodworth; Brian Bullins; Ohad Shamir; Nathan Srebro (IJCAI, 2022)

Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. - Kumar Kshitij Patel; Lingxiao Wang; Blake E. Woodworth; Brian Bullins; Nati Srebro (NeurIPS, 2022)

Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities. - Deeksha Adil; Brian Bullins; Arun Jambulapati; Sushant Sachdeva (CoRR, 2022)

Higher-Order Methods for Convex-Concave Min-Max Optimization and Monotone Variational Inequalities. - Brian Bullins; Kevin A. Lai (SIAM J. Optim., 2022)

Variance-Reduced Conservative Policy Iteration. - Naman Agarwal; Brian Bullins; Karan Singh (ALT, 2023)

Competitive Gradient Optimization. - Abhijeet Vyas; Brian Bullins; Kamyar Azizzadenesheli (ICML, 2023)

Beyond first-order methods for non-convex non-concave min-max optimization. - Abhijeet Vyas; Brian Bullins (CoRR, 2023)

Federated Composite Saddle Point Optimization. - Site Bai; Brian Bullins (CoRR, 2023)

Local Composite Saddle Point Optimization. - Site Bai; Brian Bullins (ICLR, 2024)

Faster Acceleration for Steepest Descent. - Site Bai; Brian Bullins (CoRR, 2024)

Convex optimization with p-norm oracles. - Deeksha Adil; Brian Bullins; Arun Jambulapati; Aaron Sidford (CoRR, 2024)

Faster Acceleration for Steepest Descent. - Cedar Site Bai; Brian Bullins (COLT, 2025)

Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity. - Site Bai; Brian Bullins (ICLR, 2025)

Model Immunization from a Condition Number Perspective. - Amber Yijia Zheng; Cedar Site Bai; Brian Bullins; Raymond A. Yeh (CoRR, 2025)

Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimization. - Xinyu Luo; Cedar Site Bai; Bolian Li; Petros Drineas; Ruqi Zhang; Brian Bullins (CoRR, 2025)