11 citations · 14 across the 5 of their papers we have counts for
4 papers · 1 filter
Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent
Mehdi Abbana Bennani, Thang Doan, Masashi Sugiyama
In Continual Learning settings, deep neural networks are prone to Catastrophic Forgetting. Orthogonal Gradient Descent was proposed to tackle the challenge. However, no theoretical…
-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
Masahiro Fujisawa, Takeshi Teshima, Issei Sato +1
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications. However, ABC can be sensitive to outliers if a data dis…
Support vector comparison machines
David Venuto, Toby Dylan Hocking, Lakjaree Sphanurattana +1
In ranking problems, the goal is to learn a ranking function from labeled pairs of input points. In this paper, we consider the related comparison problem, where the label indicate…
f-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models
Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
A density ratio is defined by the ratio of two probability densities. We study the inference problem of density ratios and apply a semi-parametric density-ratio estimator to the tw…