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Strong convex relaxations and mixed-integer programming formulations for trained neural networks

, , , and . (2018)cite arxiv:1811.01988.

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Parallel algebraic modeling for stochastic optimization., , and . HPTCDL@SC, page 29-35. IEEE Computer Society, (2014)978-1-4799-7020-9.The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification., , , , , and . NeurIPS, (2020)On efficient Hessian computation using the edge pushing algorithm in Julia., , , and . Optim. Methods Softw., 33 (4-6): 1010-1029 (2018)A Combinatorial Approach for Small and Strong Formulations of Disjunctive Constraints., and . Math. Oper. Res., 44 (3): 793-820 (2019)Contextual Reserve Price Optimization in Auctions., , , and . CoRR, (2020)Combinatorial Disjunctive Constraints for Obstacle Avoidance in Path Planning., , and . IROS, page 267-273. (2023)Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming., , , and . NeurIPS, (2020)Abstract: Auto-Tuning of Parallel IO Parameters for HDF5 Applications., , , , , , , , and . SC Companion, page 1430. IEEE Computer Society, (2012)Strong Mixed-Integer Programming Formulations for Trained Neural Networks., , , and . IPCO, volume 11480 of Lecture Notes in Computer Science, page 27-42. Springer, (2019)Strong convex relaxations and mixed-integer programming formulations for trained neural networks, , , and . (2018)cite arxiv:1811.01988.