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cs.LG2026
DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers
Shraman Pal, Can Li
Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches. At the same time, these proble…
cs.LG2025
Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules
Gonzalo E. Constante-Flores, Hao Chen, Can Li
Deep learning models are increasingly deployed in safety-critical tasks where predictions must satisfy hard constraints, such as physical laws, fairness requirements, or safety lim…
cs.LG2024★ 1 cited
Physics-Informed Neural Networks with Hard Linear Equality Constraints
Hao Chen, Gonzalo E. Constante Flores, Can Li
Surrogate modeling is used to replace computationally expensive simulations. Neural networks have been widely applied as surrogate models that enable efficient evaluations over com…