Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization. - Ahmet Alacaoglu; Quoc Tran-Dinh; Olivier Fercoq; Volkan Cevher (NIPS, 2017)

Almost surely constrained convex optimization. - Olivier Fercoq; Ahmet Alacaoglu; Ion Necoara; Volkan Cevher (ICML, 2019)

An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints. - Mehmet Fatih Sahin; Armin Eftekhari; Ahmet Alacaoglu; Fabian Latorre; Volkan Cevher (NeurIPS, 2019)

Random extrapolation for primal-dual coordinate descent. - Ahmet Alacaoglu; Olivier Fercoq; Volkan Cevher (ICML, 2020)

A new regret analysis for Adam-type algorithms. - Ahmet Alacaoglu; Yura Malitsky; Panayotis Mertikopoulos; Volkan Cevher (ICML, 2020)

Conditional gradient methods for stochastically constrained convex minimization. - Maria-Luiza Vladarean; Ahmet Alacaoglu; Ya-Ping Hsieh; Volkan Cevher (ICML, 2020)

Convergence of adaptive algorithms for weakly convex constrained optimization. - Ahmet Alacaoglu; Yura Malitsky; Volkan Cevher (CoRR, 2020)

An adaptive primal-dual framework for nonsmooth convex minimization. - Quoc Tran-Dinh; Ahmet Alacaoglu; Olivier Fercoq; Volkan Cevher (Math. Program. Comput., 2020)

Convergence of adaptive algorithms for constrained weakly convex optimization. - Ahmet Alacaoglu; Yura Malitsky; Volkan Cevher (NeurIPS, 2021)

Forward-reflected-backward method with variance reduction. - Ahmet Alacaoglu; Yura Malitsky; Volkan Cevher (Comput. Optim. Appl., 2021)

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond. - Ahmet Alacaoglu (2021)

Stochastic Variance Reduction for Variational Inequality Methods. - Ahmet Alacaoglu; Yura Malitsky (COLT, 2022)

A Natural Actor-Critic Framework for Zero-Sum Markov Games. - Ahmet Alacaoglu; Luca Viano; Niao He; Volkan Cevher (ICML, 2022)

On the Complexity of a Practical Primal-Dual Coordinate Method. - Ahmet Alacaoglu; Volkan Cevher; Stephen J. Wright 001 (CoRR, 2022)

Convergence and Complexity of Stochastic Subgradient Methods with Dependent Data for Nonconvex Optimization. - Ahmet Alacaoglu; Hanbaek Lyu (CoRR, 2022)

On the Convergence of Stochastic Primal-Dual Hybrid Gradient. - Ahmet Alacaoglu; Olivier Fercoq; Volkan Cevher (SIAM J. Optim., 2022)

Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data. - Ahmet Alacaoglu; Hanbaek Lyu (ICML, 2023)

Beyond the Golden Ratio for Variational Inequality Algorithms. - Ahmet Alacaoglu; Axel Böhm; Yura Malitsky (J. Mach. Learn. Res., 2023)

Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints. - Ahmet Alacaoglu; Stephen J. Wright 001 (AISTATS, 2024)

Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions. - Xufeng Cai; Ahmet Alacaoglu; Jelena Diakonikolas (ICLR, 2024)

Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity. - Ahmet Alacaoglu; Donghwan Kim; Stephen J. Wright 001 (ICML, 2024)

Extending the Reach of First-Order Algorithms for Nonconvex Min-Max Problems with Cohypomonotonicity. - Ahmet Alacaoglu; Donghwan Kim; Stephen J. Wright 001 (CoRR, 2024)

Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints. - Ruichuan Huang; Jiawei Zhang; Ahmet Alacaoglu (CoRR, 2025)

Towards Weaker Variance Assumptions for Stochastic Optimization. - Ahmet Alacaoglu; Yura Malitsky; Stephen J. Wright 001 (CoRR, 2025)