Statistical Learning
Tight Risk Bounds for Gradient Descent on Separable Data
Recently, there has been a marked increase in interest regarding the generalization capabilities of unregularized gradient-based learning methods.
Diffusion-Reward Adversarial Imitation Learning
Despite its encouraging results, GAIL training is often brittle and unstable.
Nonstationary Sparse Spectral Permanental Process
Existing permanental processes often impose constraints on kernel types or sta-tionarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel approach utilizing the sparse spectral representation of non-stationary kernels.
Effective Exploration Based on the Structural Information Principles
The SI2E analyzes value differences in the agent's policy between state-action
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
Based on our analysis, we propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.