4 papers
Sampling Complexity of TD and PPO in RKHS
Lu Zou, Wendi Ren, Weizhong Zhang +2
We revisit Proximal Policy Optimization (PPO) from a function-space perspective. Our analysis decouples policy evaluation and improvement in a reproducing kernel Hilbert space (RKH…
Self-Improving Neural-Guided Pruning: A Graph Neural Network Framework for Scalable Mixed Bundle Pricing
Liangyu Ding, Chenghan Wu, Guokai Li +1
Mixed bundle pricing is a classic revenue management problem arising in industries such as e-commerce, tourism, and video games. It refers to designing product combinations (i.e.,…
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
Lu Zou, Liang Ding
Additive Gaussian Processes (GPs) are popular approaches for nonparametric feature selection. The common training method for these models is Bayesian Back-fitting. However, the con…
Beyond State Space Representation: A General Theory for Kernel Packets
Liang Ding, Rui Tuo, Lu Zhou
Gaussian process (GP) regression provides a flexible, nonparametric framework for probabilistic modeling, yet remains computationally demanding in large-scale applications. For one…