| A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers | Hao Yu |
| A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression | JIAJIN LI · SEN HUANG · Anthony Man-Cho So |
| Acceleration via Symplectic Discretization of High-Resolution Differential Equations | Bin Shi · Simon Du · Weijie Su · Michael Jordan |
| An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums | Hadrien Hendrikx · Francis Bach · Laurent Massoulié |
| An adaptive Mirror-Prox method for variational inequalities with singular operators | Kimon Antonakopoulos · Veronica Belmega · Panayotis Mertikopoulos |
| Blended Matching Pursuit | Cyrille Combettes · Sebastian Pokutta |
| Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients | Jun Sun · Tianyi Chen · Georgios Giannakis · Zaiyue Yang |
| Complexity of Highly Parallel Non-Smooth Convex Optimization | Sebastien Bubeck · Qijia Jiang · Yin-Tat Lee · Yuanzhi Li · Aaron Sidford |
| Efficient Symmetric Norm Regression via Linear Sketching | Zhao Song · Ruosong Wang · Lin Yang · Hongyang Zhang · Peilin Zhong |
| General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme | Tao Sun · Yuejiao Sun · Dongsheng Li · Qing Liao |
| Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem | DongDong Ge · Haoyue Wang · Zikai Xiong · Yinyu Ye |
| Necessary and Sufficient Geometries for Gradient Methods | Daniel Levy · John Duchi |
| On the Curved Geometry of Accelerated Optimization | Aaron Defazio |
| Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm | Giulia Luise · Saverio Salzo · Massimiliano Pontil · Carlo Ciliberto |
| Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD | PHUONG_HA NGUYEN · Lam Nguyen · Marten van Dijk |
| Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration | Clarice Poon · Jingwei Liang |
| A Generic Acceleration Framework for Stochastic Composite Optimization | Andrei Kulunchakov · Julien Mairal |
| A unified variance-reduced accelerated gradient method for convex optimization | Guanghui Lan · Zhize Li · Yi Zhou |
| Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions | Ashia Wilson · Lester Mackey · Andre Wibisono |
| Communication trade-offs for Local-SGD with large step size | Aymeric Dieuleveut · Kumar Kshitij Patel |
| Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control | Miguel Vaquero · Jorge Cortes |
| Decentralized sketching of low rank matrices | Rakshith Sharma Srinivasa · Kiryung Lee · Marius Junge · Justin Romberg |
| Differentiable Convex Optimization Layers | Akshay Agrawal · Brandon Amos · Shane Barratt · Stephen Boyd · Steven Diamond · J. Zico Kolter |
| Dimension-Free Bounds for Low-Precision Training | Zheng Li · Christopher De Sa |
| Fast and Accurate Stochastic Gradient Estimation | Beidi Chen · Yingchen Xu · Anshumali Shrivastava |
| Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression | Deeksha Adil · Richard Peng · Sushant Sachdeva |
| Hamiltonian descent for composite objectives | Brendan O'Donoghue · Chris J. Maddison |
| High-Dimensional Optimization in Adaptive Random Subspaces | Jonathan Lacotte · Mert Pilanci · Marco Pavone |
| Optimal Stochastic and Online Learning with Individual Iterates | Yunwen Lei · Peng Yang · Ke Tang · Ding-Xuan Zhou |
| Primal-Dual Block Generalized Frank-Wolfe | Qi Lei · JIACHENG ZHUO · Constantine Caramanis · Inderjit S Dhillon · Alexandros Dimakis |
| Stochastic Frank-Wolfe for Composite Convex Minimization | Francesco Locatello · Alp Yurtsever · Olivier Fercoq · Volkan Cevher |
| Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization | Adithya M Devraj · Jianshu Chen |