{"total":23,"rows":[{"id":"2410.10807","title":"HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees","created":"2024-10-14","cited_by_count":31,"area":null},{"id":"2502.18237","title":"Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints","created":"2025-02-25","cited_by_count":18,"area":null},{"id":"2502.06774","title":"ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection","created":"2025-02-10","cited_by_count":17,"area":null},{"id":"2407.21523","title":"Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI","created":"2024-07-31","cited_by_count":15,"area":null},{"id":"2503.05954","title":"A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond","created":"2025-03-07","cited_by_count":15,"area":null},{"id":"2503.19466","title":"A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction","created":"2025-03-25","cited_by_count":11,"area":null},{"id":"2408.07579","title":"TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases","created":"2024-08-14","cited_by_count":10,"area":null},{"id":"2601.03839","title":"Logic Tensor Network-Enhanced Generative Adversarial Network","created":"2026-01-07","cited_by_count":3,"area":null},{"id":"2402.18285","title":"PiShield: A PyTorch Package for Learning with Requirements","created":"2024-02-28","cited_by_count":2,"area":null},{"id":"2603.02015","title":"CausalWrap: Model-Agnostic Causal Constraint Wrappers for Tabular Synthetic Data","created":"2026-03-02","cited_by_count":2,"area":null},{"id":"2409.12642","title":"Deep generative models as an adversarial attack strategy for tabular machine learning","created":"2024-09-19","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":"2512.07306","title":"Exact Synthetic Populations for Scalable Societal and Market Modeling","created":"2025-12-08","cited_by_count":1,"area":null},{"id":"2603.23016","title":"A Sobering Look at Tabular Data Generation via Probabilistic Circuits","created":"2026-03-24","cited_by_count":1,"area":null},{"id":"2505.05019","title":"Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints","created":"2025-05-08","cited_by_count":0,"area":null},{"id":"2506.12911","title":"Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2506.15506","title":"Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2509.09960","title":"Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes","created":"2025-09-12","cited_by_count":0,"area":null},{"id":"2602.07875","title":"Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion","created":"2026-02-08","cited_by_count":0,"area":null},{"id":"2604.04195","title":"Stable and Privacy-Preserving Synthetic Educational Data with Empirical Marginals: A Copula-Based Approach","created":"2026-04-05","cited_by_count":0,"area":null},{"id":"2605.24437","title":"CAffNet: Hard Constraint-Affine Neural Networks","created":"2026-05-23","cited_by_count":0,"area":null},{"id":"2606.13803","title":"Neural Slack Variables for Shape Constraints","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2608.21605","title":"Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANs","created":"2026-08-21","cited_by_count":0,"area":null}],"next_cursor":null}