Nadav Hallak

According to our database, Nadav Hallak authored at least 16 papers between 2016 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

An Augmented Lagrangian Approach to Bi-Level Optimization via a Smooth Equilibrium Constrained Problem.

Nadav Hallak, Nitay Suissa

J. Optim. Theory Appl., March, 2026

2025

A Stochastic Approach to the Subset Selection Problem via Mirror Descent.

Dan Greenstein, Elazar Gershuni, Ilan Ben-Bassat, Yaroslav Fyodorov, Ran Moshe, Fiana Raiber, Alex Shtoff, Oren Somekh, Nadav Hallak

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

A Path-Based Approach to Constrained Sparse Optimization.

Nadav Hallak

SIAM J. Optim., March, 2024

A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle.

Nadav Hallak, Kfir Yehuda Levy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

An Adaptive Lagrangian-Based Scheme for Nonconvex Composite Optimization.

Nadav Hallak, Marc Teboulle

Math. Oper. Res., 2023

2022

The regularized feasible directions method for nonconvex optimization.

Amir Beck, Nadav Hallak

Oper. Res. Lett., 2022

A Dynamic Alternating Direction of Multipliers for Nonconvex Minimization with Nonlinear Functional Equality Constraints.

Eyal Cohen, Nadav Hallak, Marc Teboulle

J. Optim. Theory Appl., 2022

2021

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach.

Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

On the Convergence to Stationary Points of Deterministic and Randomized Feasible Descent Directions Methods.

Amir Beck, Nadav Hallak

SIAM J. Optim., 2020

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach.

Nadav Hallak, Marc Teboulle

J. Optim. Theory Appl., 2020

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems.

Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Proximal Mapping of the 1-path-norm of Shallow Networks.

Fabian Latorre, Paul Rolland, Nadav Hallak, Volkan Cevher

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

A non-Euclidean gradient descent method with sketching for unconstrained matrix minimization.

Nadav Hallak, Marc Teboulle

Oper. Res. Lett., 2019

Optimization problems involving group sparsity terms.

Amir Beck, Nadav Hallak

Math. Program., 2019

2018

Proximal Mapping for Symmetric Penalty and Sparsity.

Amir Beck, Nadav Hallak

SIAM J. Optim., 2018

2016

On the Minimization Over Sparse Symmetric Sets: Projections, Optimality Conditions, and Algorithms.

Amir Beck, Nadav Hallak

Math. Oper. Res., 2016