{"total":37,"rows":[{"id":"2305.00241","title":"When Deep Learning Meets Polyhedral Theory: A Survey","created":"2023-04-29","cited_by_count":39,"area":null},{"id":"2202.02414","title":"OMLT: Optimization & Machine Learning Toolkit","created":"2022-02-04","cited_by_count":35,"area":null},{"id":"2501.00200","title":"Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes","created":"2024-12-31","cited_by_count":33,"area":null},{"id":"2402.07251","title":"Physics-Informed Neural Networks with Hard Linear Equality Constraints","created":"2024-02-11","cited_by_count":30,"area":null},{"id":"2201.10035","title":"Maximizing information from chemical engineering data sets: Applications to machine learning","created":"2022-01-25","cited_by_count":17,"area":null},{"id":"2205.14189","title":"Optimizing Objective Functions from Trained ReLU Neural Networks via Sampling","created":"2022-05-27","cited_by_count":11,"area":null},{"id":"2401.03451","title":"Optimization Over Trained Neural Networks: Taking a Relaxing Walk","created":"2024-01-07","cited_by_count":10,"area":null},{"id":"2402.13937","title":"Verifying message-passing neural networks via topology-based bounds tightening","created":"2024-02-21","cited_by_count":10,"area":null},{"id":"2111.10489","title":"Modeling Design and Control Problems Involving Neural Network Surrogates","created":"2021-11-20","cited_by_count":9,"area":null},{"id":"2401.13836","title":"Machine learning for industrial sensing and control: A survey and practical perspective","created":"2024-01-24","cited_by_count":8,"area":null},{"id":"2202.05198","title":"P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints","created":"2022-02-10","cited_by_count":7,"area":null},{"id":"2206.09149","title":"Piecewise Linear Neural Networks and Deep Learning","created":"2022-06-18","cited_by_count":7,"area":null},{"id":"2406.13433","title":"Certification for Differentially Private Prediction in Gradient-Based Training","created":"2024-06-19","cited_by_count":7,"area":null},{"id":"2312.16699","title":"Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks","created":"2023-12-27","cited_by_count":6,"area":null},{"id":"2402.06697","title":"Feed-Forward Neural Networks as a Mixed-Integer Program","created":"2024-02-09","cited_by_count":4,"area":null},{"id":"2109.14041","title":"Local Repair of Neural Networks Using Optimization","created":"2021-09-28","cited_by_count":3,"area":null},{"id":"2512.01899","title":"Provably Safe Model Updates","created":"2025-12-01","cited_by_count":3,"area":null},{"id":"2103.00507","title":"Where the Action is: Let's make Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problems work!","created":"2021-02-28","cited_by_count":2,"area":null},{"id":"2302.10344","title":"Model-based feature selection for neural networks: A mixed-integer programming approach","created":"2023-02-20","cited_by_count":2,"area":null},{"id":"2410.06816","title":"Expressiveness of Multi-Neuron Convex Relaxations in Neural Network Certification","created":"2024-10-09","cited_by_count":2,"area":null},{"id":"2505.23640","title":"Global optimization of graph acquisition functions for neural architecture search","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2604.22746","title":"Relaxation-Informed Training of Neural Network Surrogate Models","created":"2026-04-24","cited_by_count":2,"area":null},{"id":"2112.07007","title":"Optimizing over an ensemble of neural networks","created":"2021-12-13","cited_by_count":1,"area":null},{"id":"2201.04429","title":"Constraint Learning to Define Trust Regions in Predictive-Model Embedded Optimization","created":"2022-01-12","cited_by_count":1,"area":null},{"id":"2212.03659","title":"Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks","created":"2022-12-07","cited_by_count":1,"area":null},{"id":"2302.00990","title":"Physics Informed Piecewise Linear Neural Networks for Process Optimization","created":"2023-02-02","cited_by_count":1,"area":null},{"id":"2409.20517","title":"SMLE: Safe Machine Learning via Embedded Overapproximation","created":"2024-09-30","cited_by_count":1,"area":null},{"id":"2506.03531","title":"Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees","created":"2025-06-04","cited_by_count":1,"area":null},{"id":"2606.07728","title":"Characterizing the Discrete Geometry of ReLU Networks","created":"2026-06-05","cited_by_count":1,"area":null},{"id":"2306.02639","title":"Evaluating robustness of support vector machines with the Lagrangian dual approach","created":"2023-06-05","cited_by_count":0,"area":null},{"id":"2409.13770","title":"A constrained optimization approach to improve robustness of neural networks","created":"2024-09-18","cited_by_count":0,"area":null},{"id":"2410.23362","title":"Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations","created":"2024-10-30","cited_by_count":0,"area":null},{"id":"2505.01985","title":"Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate","created":"2025-05-04","cited_by_count":0,"area":null},{"id":"2605.00855","title":"An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions","created":"2026-04-21","cited_by_count":0,"area":null},{"id":"2605.23151","title":"Convex Hybrid Modeling: An Operator-Based Approach","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2609.32018","title":"Representation Learning for Exact Preimages","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.33879","title":"DCEmbed: Scalable Optimization over Neural Surrogates","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}