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Found 6 papers, 1 papers with code
Minimax Optimal Transfer Learning for Kernel-based Nonparametric Regression
This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert space.
Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift
Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used.
Relative Entropy Gradient Sampler for Unnormalized Distributions
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions.
Non-Crossing Quantile Regression for Distributional Reinforcement Learning
Distributional reinforcement learning (DRL) estimates the distribution over future returns instead of the mean to more efficiently capture the intrinsic uncertainty of MDPs.
Metric learning by Similarity Network for Deep Semi-Supervised Learning
Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning.
Structure learning via unstructured kernel-based M-regression
In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis.
