{"total":20,"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":"2206.05825","title":"A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games","created":"2022-06-12","cited_by_count":66,"area":null},{"id":"2105.02343","title":"CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints","created":"2021-05-05","cited_by_count":49,"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":"2302.01971","title":"How Bad is Top-$K$ Recommendation under Competing Content Creators?","created":"2023-02-03","cited_by_count":27,"area":null},{"id":"2103.04539","title":"Model-Free Online Learning in Unknown Sequential Decision Making Problems and Games","created":"2021-03-08","cited_by_count":12,"area":null},{"id":"2210.09257","title":"Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers","created":"2022-10-17","cited_by_count":10,"area":null},{"id":"2410.06324","title":"Differentiation Through Black-Box Quadratic Programming Solvers","created":"2024-10-08","cited_by_count":10,"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":"2108.07472","title":"Is Nash Equilibrium Approximator Learnable?","created":"2021-08-17","cited_by_count":4,"area":null},{"id":"2310.02442","title":"GenCO: Generating Diverse Designs with Combinatorial Constraints","created":"2023-10-03","cited_by_count":3,"area":null},{"id":"2108.04578","title":"Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP","created":"2021-08-10","cited_by_count":2,"area":null},{"id":"2301.11481","title":"Are Equivariant Equilibrium Approximators Beneficial?","created":"2023-01-27","cited_by_count":2,"area":null},{"id":"2603.07705","title":"Deep Incentive Design with Differentiable Equilibrium Blocks","created":"2026-03-08","cited_by_count":2,"area":null},{"id":"2308.08017","title":"Active Inverse Learning in Stackelberg Trajectory Games","created":"2023-08-15","cited_by_count":1,"area":null},{"id":"2406.00779","title":"Differentiation of Multi-objective Data-driven Decision Pipeline","created":"2024-06-02","cited_by_count":1,"area":null},{"id":"2106.00906","title":"Operator Splitting for Learning to Predict Equilibria in Convex Games","created":"2021-06-02","cited_by_count":0,"area":null},{"id":"2112.15151","title":"From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactions","created":"2021-12-30","cited_by_count":0,"area":null},{"id":"2605.21341","title":"Semiparametric Efficient Bilevel Gradient Estimation","created":"2026-05-20","cited_by_count":0,"area":null}],"next_cursor":null}