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Dual Certified White-Box Inference for Input Convex Neural Networks
Kang Liu, Jianchen Hu, Wei Peng
Input convex neural networks (ICNNs) are used to learn convex objectives whose minimizers define decisions, making efficient and reliable optimization central to inference. At nons…
Deep Neural ODE Operator Networks for PDEs
Ziqian Li, Kang Liu, Yongcun Song +2
Operator learning has emerged as a promising paradigm for developing efficient surrogate models to solve partial differential equations (PDEs). However, existing approaches often o…
Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics
Kang Liu, Enrique Zuazua
In this work, we address three non-convex optimization problems associated with the training of shallow neural networks (NNs) for exact and approximate representation, as well as f…
Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning
Kang Liu, Ziqi Wang, Enrique Zuazua
In federated learning (FL), a central server typically allocates training efforts to clients. However, from a market-oriented perspective, clients may independently choose their tr…