450 citations · 1.2k across the 59 of their papers we have counts for
12 papers · 1 filter
Post-selection inference with HSIC-Lasso
Tobias Freidling, Benjamin Poignard, Héctor Climente-González +1
Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning. Variable selection methods have thus…
Poincare: Recommending Publication Venues via Treatment Effect Estimation
Ryoma Sato, Makoto Yamada, Hisashi Kashima
Choosing a publication venue for an academic paper is a crucial step in the research process. However, in many cases, decisions are based solely on the experience of researchers, w…
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Vu Nguyen, Tam Le, Makoto Yamada +1
Neural architecture search (NAS) automates the design of deep neural networks. One of the main challenges in searching complex and non-continuous architectures is to compare the si…
Neural Methods for Point-wise Dependency Estimation
Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada +2
Since its inception, the neural estimation of mutual information (MI) has demonstrated the empirical success of modeling expected dependency between high-dimensional random variabl…
Fast Unbalanced Optimal Transport on a Tree
Ryoma Sato, Makoto Yamada, Hisashi Kashima
This study examines the time complexities of the unbalanced optimal transport problems from an algorithmic perspective for the first time. We reveal which problems in unbalanced op…
Feature Robust Optimal Transport for High-dimensional Data
Mathis Petrovich, Chao Liang, Ryoma Sato +6
Optimal transport is a machine learning problem with applications including distribution comparison, feature selection, and generative adversarial networks. In this paper, we propo…