Despite the fundamental nature of the inhomogeneous Poisson process in the
theory and application of stochastic processes, and its attractive
generalizations (e.g. Cox process), few tractable nonparametric modeling
approaches of intensity functions exist, especially when observed points lie in
a high-dimensional space. In this paper we develop a new, computationally
tractable Reproducing Kernel Hilbert Space (RKHS) formulation for the
inhomogeneous Poisson process. We model the square root of the intensity as an
RKHS function. Whereas RKHS models used in supervised learning rely on the
so-called representer theorem, the form of the inhomogeneous Poisson process
likelihood means that the representer theorem does not apply. However, we prove
that the representer theorem does hold in an appropriately transformed RKHS,
guaranteeing that the optimization of the penalized likelihood can be cast as a
tractable finite-dimensional problem. The resulting approach is simple to
implement, and readily scales to high dimensions and large-scale datasets.

本文提出了一种新的、可计算的再生核希尔伯特空间 (RKHS) 公式，用于建模不均匀泊松过程，无需使用代表定理就能完成多维空间下的非参数建模，并证明了 RKHS 在适当转换下满足代表定理，可以将惩罚似然的优化转化为易于处理的有限维问题，该方法易于实现，适用于高维和大型数据集。

基于复制核的泊松强度估计

Poisson intensity estimation with reproducing kernels

This paper presents a novel approach for incremental semiparametric inverse
dynamics learning. In particular, we consider the mixture of two approaches:
Parametric modeling based on rigid body dynamics equations and nonparametric
modeling based on incremental kernel methods, with no prior information on the
mechanical properties of the system. This yields to an incremental
semiparametric approach, leveraging the advantages of both the parametric and
nonparametric models. We validate the proposed technique learning the dynamics
of one arm of the iCub humanoid robot.

本文提出了一种新颖的增量半参数反向动力学学习方法，通过刚体动力学方程的参数建模和增量核方法的非参数建模相结合，实现了对机械系统动力学的增量半参数学习，并在 iCub 人型机器人贴身进行验证。

增量半参数反动力学学习

Incremental Semiparametric Inverse Dynamics Learning

Some images that are difficult to recognize on their own may become more
clear in the context of a neighborhood of related images with similar
social-network metadata. We build on this intuition to improve multilabel image
annotation. Our model uses image metadata nonparametrically to generate
neighborhoods of related images using Jaccard similarities, then uses a deep
neural network to blend visual information from the image and its neighbors.
Prior work typically models image metadata parametrically, in contrast, our
nonparametric treatment allows our model to perform well even when the
vocabulary of metadata changes between training and testing. We perform
comprehensive experiments on the NUS-WIDE dataset, where we show that our model
outperforms state-of-the-art methods for multilabel image annotation even when
our model is forced to generalize to new types of metadata.

利用社交网络元数据构建图像社区，结合深度神经网络，使用 Jacard 相似性衡量图像间的关系，实现多标签图像注释的非参模型，相比参数模型更加稳健。

利用图像元数据进行图像注释：关爱邻里

Love Thy Neighbors: Image Annotation by Exploiting Image Metadata

We consider stationary hidden Markov models with finite state space and
nonparametric modeling of the emission distributions. It has remained unknown
until very recently that such models are identifiable. In this paper, we
propose a new penalized least-squares esti-mator for the emission distributions
which is statistically optimal and practically tractable. We prove a non
asymptotic oracle inequality for our nonparametric estimator of the emission
distributions. A consequence is that this new estimator is rate minimax
adaptive up to a logarithmic term. Our methodology is based on projections of
the emission distributions onto nested subspaces of increasing complexity. The
popular spectral estimators are unable to achieve the optimal rate but may be
used as initial points in our procedure. Simulations are given that show the
improvement obtained when applying the least-squares minimization consecutively
to the spectral estimation.

本文提出了一种新的带罚项的最小二乘估计器，用于发射分布的非参数估计，并证明了其可以达到速率最小极致适应性。该方法基于逐步增加复杂度的嵌套子空间的发射分布的投影，最新的模拟表明了最小二乘估计法的改进。