2 papers
stat.ML2026
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…
cs.LG2026
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…