13 citations · 33 across the 25 of their papers we have counts for
7 papers · 1 filter
An integrated perspective of robustness in regression through the lens of the bias-variance trade-off
Akifumi Okuno
This paper presents an integrated perspective on robustness in regression. Specifically, we examine the relationship between traditional outlier-resistant robust estimation and rob…
A multivariate adaptation of direct kernel estimator of density ratio
Akifumi Okuno
Ćwik and Mielniczuk (1989) introduced a univariate kernel density ratio estimator, which directly estimates the ratio without estimating the two densities of interest. This study p…
Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdown
Akifumi Okuno, Shotaro Yagishita
In this study, we tackle the challenge of outlier-robust predictive modeling using highly expressive neural networks. Our approach integrates two key components: (1) a transformed…
Minimizing robust density power-based divergences for general parametric density models
Akifumi Okuno
Density power divergence (DPD) is designed to robustly estimate the underlying distribution of observations, in the presence of outliers. However, DPD involves an integral of the p…
Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series
Akifumi Okuno, Yuya Morishita, Yoh-ichi Mototake
This study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function. Traditional approaches in this…
An interpretable neural network-based non-proportional odds model for ordinal regression
Akifumi Okuno, Kazuharu Harada
This study proposes an interpretable neural network-based non-proportional odds model (NPOM) for ordinal regression. NPOM is different from conventional approaches to ordin…
A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates
Akifumi Okuno, Kohei Hattori
In this study, we examine a clustering problem in which the covariates of each individual element in a dataset are associated with an uncertainty specific to that element. More spe…