4 papers · 1 filter
Residual-Controlled Multiplier Learning for Stochastic Constrained Decision-Making
Kang Liu, Jianchen Hu, Ziyu Qu +3
Stochastic constrained decision-making requires optimizing performance objectives while enforcing statistical requirements such as safety or fairness. However, standard primal--dua…
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…
SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions
Kang Liu, Jianchen Hu, Wei Peng
Classical ReLU-based Input Convex Neural Networks (ICNNs) are equivalent to the optimal value functions of Linear Programming (LP). This intrinsic structural equivalence restricts…
OPBO: Order-Preserving Bayesian Optimization
Wei Peng, Jianchen Hu, Kang Liu +1
Bayesian optimization is an effective method for solving expensive black-box optimization problems. Most existing methods use Gaussian processes (GP) as the surrogate model for app…