227 citations · 363 across the 18 of their papers we have counts for
18 papers
Ratio-based Loss Functions
Lena Helgerth, Andreas Christmann
Algorithms in machine learning and AI do critically depend on at least three key components: (i) the risk function, which is the expectation of the loss function, (ii) the function…
Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions
Jinhui Bai, Andreas Christmann, Lei Shi
In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compared to traditional kernel SGD, ou…
Bootstrap SGD: Algorithmic Stability and Robustness
Andreas Christmann, Yunwen Lei
In this paper some methods to use the empirical bootstrap approach for stochastic gradient descent (SGD) to minimize the empirical risk over a separable Hilbert space are investiga…
Optimality of Robust Online Learning
Zheng-Chu Guo, Andreas Christmann, Lei Shi
In this paper, we study an online learning algorithm with a robust loss function for regression over a reproducing kernel Hilbert space (RKHS). The loss function $\…
Total Stability of SVMs and Localized SVMs
Hannes Köhler, Andreas Christmann
Regularized kernel-based methods such as support vector machines (SVMs) typically depend on the underlying probability measure (respectively an empirical measure $\mat…
On the robustness of kernel-based pairwise learning
Patrick Gensler, Andreas Christmann
It is shown that many results on the statistical robustness of kernel-based pairwise learning can be derived under basically no assumptions on the input and output spaces. In parti…