Publications

Publications

For the latest list please refer to my google scholar profile

Old List:

• Amir Beck and Nadav Hallak. "The Regularized Feasible Directions Method for Nonconvex Optimization." Operations Research Letters (2022).
  [paper]

• Eyal Cohen, Nadav Hallak, and Marc Teboulle. "A Dynamic Alternating Direction of Multipliers for Nonconvex Minimization with Nonlinear
  Functional Equality Constraints." Journal of Optimization Theory and Applications. 2021 Sep 13:1-30. 

[paper]

• Nadav Hallak,  Panayotis Mertikopoulos, and Volkan Cevher. "Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach." ICML '21. International Conference on Machine Learning. PMLR, 2021.
  [paper]

• Panayotis Mertikopoulos,  Nadav Hallak,  Ali Kavis, and Volkan Cevher. "On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems ." In NeurIPS '20: Proceedings of the 34th International Conference on Neural Information Processing Systems, 2020.
  [paper]

• Fabian Latorre, Paul Rolland, Nadav Hallak, and Volkan Cevher. "Efficient proximal mapping of the 1-path-norm of shallow networks." In ICML '20: Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5651-5661, 2020.
  [paper]

• Nadav Hallak, and Marc Teboulle. “Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach.” Journal of Optimization Theory and Applications  (JOTA). 186, 480–503 (2020).
  [submitted version] [paper]

• Nadav Hallak, and Marc Teboulle. “A Non-Euclidean Gradient Descent Method with Sketching for Unconstrained Matrix Minimization .” Operations Research Letters 47, no. 5 (2019): 421-426.
  [submitted version] [published version]

• Beck Amir, and Nadav Hallak. “On the Convergence to Stationary Points of Deterministic and Randomized Feasible Descent Directions Methods .” SIAM Journal on Optimization. Accepted November 2019 (Submitted in 2018).
  [submitted version] [published version]

• Beck Amir, and Nadav Hallak. “Optimization Problems Involving Group Sparsity Terms .”  Mathematical Programming (2017): 1-29. 
  [submitted version] [published version]

• Beck, Amir, and Nadav Hallak. "Proximal Mapping for Symmetric Penalty and Sparsity." SIAM Journal on Optimization 28.1 (2018): 496-527.
  [submitted version] [published version]

• Beck Amir, and Nadav Hallak. “On the minimization over sparse symmetric sets: Projections, optimality conditions, and algorithms.” Mathematics of Operations Research 41.1 (2015): 196-223.
  [submitted version] [published version]

Workshops

• Efficient Proximal Mapping of the 1-path-norm of Shallow Networks is presented in the Sparsity in Neural Networks: Advancing Understanding and Practice (SNN2021) workshop. [link]

Slides

• ICML2021 slides on Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach: my slides here.

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