Software

A list of software deleveloped with associated papers.

HR-NN

Holistic Robust (HR) learning provides certified protection against data poisoning and evasion attacks, while enjoying guaranteed generalization. HR-NN is a python package implemeting HR for deep neural network training.

• Bennouna, M.A., R. Lucas, and B.P.G. Van Parys (2023). “Certified robust neural networks: Generalization and corruption resistance”. In: International Conference on Machine Learning (ICML). Honolulu, Hawaii, USA, pp. 2092–2112. Link

• Bennouna, M.A. and B.P.G. Van Parys (2022). “Holistic robust data-driven decisions”. In: Mathematical Programming. Submitted. Finalist for 2023 INFORMS Data Mining Best Paper Award Competition. Link

BnB-PEP

This code can be used to reproduce and verify the results from the work

• Das Gupta, S., B.P.G. Van Parys, and E.K. Ryu (2023). “Branch-and-bound performance estimation programming: A unified methodology for construction optimal optimization methods”. In: Mathematical Programming. Appeared also in Quanta Magazine. Link

DuSA

DuSA.jl is a Julia package which implements an optimal exploitation/exploration in DUal Structured Algorithm.

• Van Parys, B.P.G. and N. Golreaei (2022). “Optimal learning for structured bandits”. In: Management Science. Accepted. Link

NExOS

NExOS.jl is a Julia package that implements the Nonconvex Exterior-point Optimization Solver (NExOS). The package is tailored for minimizing a convex cost function over a nonconvex constraint set.

• Das Gupta, S., B. Stellato, and B.P.G. Van Parys (2022). “Exterior-point optimization for nonconvex learning”. In: SIAM Journal of Optimization. Submitted. Link