8 papers
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…
Exact Dual Geometry of SOC-ICNN Value Functions
Kang Liu, Jianchen Hu, Wei Peng
Input Convex Neural Networks (ICNNs) are commonly used in a two-stage manner: one first trains a convex network and then minimizes it over its input in a downstream inference probl…
Budget-aware Auto Optimizer Configurator
Kang Liu, Wei Peng, Jianchen Hu
Optimizer states occupy massive GPU memory in large-scale model training. However, gradients in different network blocks exhibit distinct behaviors, such as varying directional sta…
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…
Biomed-DPT: Dual Modality Prompt Tuning for Biomedical Vision-Language Models
Wei Peng, Kang Liu, Jianchen Hu +1
Prompt learning is one of the most effective paradigms for adapting pre-trained vision-language models (VLMs) to the biomedical image classification tasks in few shot scenarios. Ho…