Mirrored Langevin Dynamics. - Ya-Ping Hsieh; Ali Kavis; Paul Rolland; Volkan Cevher (NeurIPS, 2018)

Efficient learning of smooth probability functions from Bernoulli tests with guarantees. - Paul Rolland; Ali Kavis; Adish Singla; Volkan Cevher (CoRR, 2018)

Efficient learning of smooth probability functions from Bernoulli tests with guarantees. - Paul Rolland; Ali Kavis; Alexander Immer; Adish Singla; Volkan Cevher (ICML, 2019)

UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. - Ali Kavis; Kfir Y. Levy; Francis R. Bach; Volkan Cevher (NeurIPS, 2019)

Double-Loop Unadjusted Langevin Algorithm. - Paul Rolland; Armin Eftekhari; Ali Kavis; Volkan Cevher (ICML, 2020)

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems. - Panayotis Mertikopoulos; Nadav Hallak; Ali Kavis; Volkan Cevher (NeurIPS, 2020)

Sifting through the noise: Universal first-order methods for stochastic variational inequalities. - Kimon Antonakopoulos; Thomas Pethick; Ali Kavis; Panayotis Mertikopoulos; Volkan Cevher (NeurIPS, 2021)

STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization. - Kfir Y. Levy; Ali Kavis; Volkan Cevher (NeurIPS, 2021)

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization. - Kfir Y. Levy; Ali Kavis; Volkan Cevher (CoRR, 2021)

High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize. - Ali Kavis; Kfir Yehuda Levy; Volkan Cevher (ICLR, 2022)

Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods. - Kimon Antonakopoulos; Ali Kavis; Volkan Cevher (NeurIPS, 2022)

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization. - Ali Kavis; Stratis Skoulakis; Kimon Antonakopoulos; Leello Tadesse Dadi; Volkan Cevher (NeurIPS, 2022)

Alternation makes the adversary weaker in two-player games. - Volkan Cevher; Ashok Cutkosky; Ali Kavis; Georgios Piliouras; Stratis Skoulakis; Luca Viano (NeurIPS, 2023)

Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness. - Artem Agafonov; Dmitry Kamzolov; Alexander V. Gasnikov; Ali Kavis; Kimon Antonakopoulos; Volkan Cevher; Martin Takác 001 (ICLR, 2024)

Universal Gradient Methods for Stochastic Convex Optimization. - Anton Rodomanov; Ali Kavis; Yongtao Wu; Kimon Antonakopoulos; Volkan Cevher (ICML, 2024)

Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. - Ruichen Jiang; Ali Kavis; Qiujiang Jin; Sujay Sanghavi; Aryan Mokhtari (NeurIPS, 2024)

Understanding Contrastive Learning via Gaussian Mixture Models. - Parikshit Bansal; Ali Kavis; Sujay Sanghavi (CoRR, 2024)

Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting. - Sunny Sanyal; Hayden Prairie; Rudrajit Das; Ali Kavis; Sujay Sanghavi (CoRR, 2025)

Online Learning-guided Learning Rate Adaptation via Gradient Alignment. - Ruichen Jiang; Ali Kavis; Aryan Mokhtari (CoRR, 2025)