{"total":19,"rows":[{"id":"2104.12225","title":"DC3: A learning method for optimization with hard constraints","created":"2021-04-25","cited_by_count":184,"area":null},{"id":"2110.01515","title":"A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning","created":"2021-10-04","cited_by_count":82,"area":null},{"id":"2105.08881","title":"Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization","created":"2021-05-19","cited_by_count":31,"area":null},{"id":"2305.16830","title":"Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize","created":"2023-05-26","cited_by_count":18,"area":null},{"id":"2106.10349","title":"The Perils of Learning Before Optimizing","created":"2021-06-18","cited_by_count":15,"area":null},{"id":"2203.16067","title":"Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses","created":"2022-03-30","cited_by_count":12,"area":null},{"id":"2203.01693","title":"Learning Neural Set Functions Under the Optimal Subset Oracle","created":"2022-03-03","cited_by_count":10,"area":null},{"id":"2312.11522","title":"Assessing SATNet's Ability to Solve the Symbol Grounding Problem","created":"2023-12-13","cited_by_count":10,"area":null},{"id":"2105.03417","title":"Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference","created":"2021-05-07","cited_by_count":7,"area":null},{"id":"2111.06961","title":"Adversarially Robust Learning for Security-Constrained Optimal Power Flow","created":"2021-11-12","cited_by_count":6,"area":null},{"id":"2112.08091","title":"Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems","created":"2021-12-15","cited_by_count":6,"area":null},{"id":"2210.11033","title":"Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection","created":"2022-10-20","cited_by_count":6,"area":null},{"id":"2111.01262","title":"Minimax Optimization: The Case of Convex-Submodular","created":"2021-11-01","cited_by_count":5,"area":null},{"id":"2402.03139","title":"Enhancing Neural Subset Selection: Integrating Background Information into Set Representations","created":"2024-02-05","cited_by_count":1,"area":null},{"id":"2403.08199","title":"Deep Submodular Peripteral Networks","created":"2024-03-13","cited_by_count":1,"area":null},{"id":"2502.05416","title":"Deep Generative Models with Hard Linear Equality Constraints","created":"2025-02-08","cited_by_count":1,"area":null},{"id":"2512.11534","title":"HFS: Holistic Query-Aware Frame Selection for Efficient Video Understanding","created":"2025-12-12","cited_by_count":1,"area":null},{"id":"2602.03297","title":"Lipschitz Multiscale Deep Equilibrium Models: A Theoretically Guaranteed and Accelerated Approach","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2607.11555","title":"Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions","created":"2026-07-13","cited_by_count":0,"area":null}],"next_cursor":null}