b'Makoto Yamada'

research

∙ 07/28/2023

Implicit neural representation for change detection

Detecting changes that occurred in a pair of 3D airborne LiDAR point clo...

0 Peter Naylor, et al. ∙

research

∙ 05/19/2023

Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence

Decentralized learning has recently been attracting increasing attention...

0 Yuki Takezawa, et al. ∙

research

∙ 02/20/2023

Nystrom Method for Accurate and Scalable Implicit Differentiation

The essential difficulty of gradient-based bilevel optimization using im...

0 Ryuichiro Hataya, et al. ∙

research

∙ 02/14/2023

Optimal Transport for Change Detection on LiDAR Point Clouds

The detection of changes occurring in multi-temporal remote sensing data...

9 Marco Fiorucci, et al. ∙

research

∙ 02/04/2023

Structural Explanations for Graph Neural Networks using HSIC

Graph neural networks (GNNs) are a type of neural model that tackle grap...

0 Ayato Toyokuni, et al. ∙

research

∙ 09/30/2022

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data

SGD with momentum acceleration is one of the key components for improvin...

0 Yuki Takezawa, et al. ∙

research

∙ 08/21/2022

Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling

The research process includes many decisions, e.g., how to entitle and w...

0 Ryoma Sato, et al. ∙

research

∙ 08/08/2022

Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images

The generation of three-dimensional (3D) medical images can have great a...

6 Yanbin Liu, et al. ∙

research

∙ 07/22/2022

Scale dependant layer for self-supervised nuclei encoding

Recent developments in self-supervised learning give us the possibility ...

0 Peter Naylor, et al. ∙

research

∙ 06/24/2022

Approximating 1-Wasserstein Distance with Trees

Wasserstein distance, which measures the discrepancy between distributio...

9 Makoto Yamada, et al. ∙

research

∙ 06/01/2022

Feature Selection for Discovering Distributional Treatment Effect Modifiers

Finding the features relevant to the difference in treatment effects is ...

0 Yoichi Chikahara, et al. ∙

research

∙ 05/23/2022

Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization

In recent years, decentralized learning has emerged as a powerful tool n...

0 Yuki Takezawa, et al. ∙

research

∙ 05/08/2022

Communication Compression for Decentralized Learning with Operator Splitting Methods

In decentralized learning, operator splitting methods using a primal-dua...

0 Yuki Takezawa, et al. ∙

research

∙ 10/16/2021

Nys-Curve: Nyström-Approximated Curvature for Stochastic Optimization

The quasi-Newton methods generally provide curvature information by appr...

0 Hardik Tankaria, et al. ∙

research

∙ 09/30/2021

Adversarial Regression with Doubly Non-negative Weighting Matrices

Many machine learning tasks that involve predicting an output response c...

0 Tam Le, et al. ∙

research

∙ 09/08/2021

Fixed Support Tree-Sliced Wasserstein Barycenter

The Wasserstein barycenter has been widely studied in various fields, in...

18 Yuki Takezawa, et al. ∙

research

∙ 05/30/2021

Re-evaluating Word Mover's Distance

The word mover's distance (WMD) is a fundamental technique for measuring...

0 Ryoma Sato, et al. ∙

research

∙ 03/01/2021

Computationally Efficient Wasserstein Loss for Structured Labels

The problem of estimating the probability distribution of labels has bee...

0 Ayato Toyokuni, et al. ∙

research

∙ 02/08/2021

Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares

A recently introduced technique for a sparse optimization problem called...

0 Hiroaki Yamada, et al. ∙

research

∙ 01/27/2021

Supervised Tree-Wasserstein Distance

To measure the similarity of documents, the Wasserstein distance is a po...

0 Yuki Takezawa, et al. ∙

research

∙ 10/29/2020

Post-selection inference with HSIC-Lasso

Detecting influential features in complex (non-linear and/or high-dimens...

0 Tobias Freidling, et al. ∙

research

∙ 10/19/2020

Poincare: Recommending Publication Venues via Treatment Effect Estimation

Choosing a publication venue for an academic paper is a crucial step in ...

0 Ryoma Sato, et al. ∙

research

∙ 06/13/2020

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search

Neural architecture search (NAS) automates the design of deep neural net...

0 Vu Nguyen, et al. ∙

research

∙ 06/09/2020

Neural Methods for Point-wise Dependency Estimation

Since its inception, the neural estimation of mutual information (MI) ha...

13 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 06/04/2020

Fast Unbalanced Optimal Transport on Tree

This study examines the time complexities of the unbalanced optimal tran...

0 Ryoma Sato, et al. ∙

research

∙ 05/25/2020

Feature Robust Optimal Transport for High-dimensional Data

Optimal transport is a machine learning technique with applications incl...

13 Mathis Petrovich, et al. ∙

research

∙ 03/25/2020

Volumization as a Natural Generalization of Weight Decay

We propose a novel regularization method, called volumization, for neura...

2 Liu Ziyin, et al. ∙

research

∙ 02/08/2020

Random Features Strengthen Graph Neural Networks

Graph neural networks (GNNs) are powerful machine learning models for va...

66 Ryoma Sato, et al. ∙

research

∙ 02/05/2020

Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces

The problem of comparing distributions endowed with their own geometry a...

2 Ryoma Sato, et al. ∙

research

∙ 01/23/2020

FsNet: Feature Selection Network on High-dimensional Biological Data

Biological data are generally high-dimensional and require efficient mac...

26 Dinesh Singh, et al. ∙

research

∙ 01/17/2020

GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

Graph structured data has wide applicability in various domains such as ...

22 Qiang Huang, et al. ∙

research

∙ 10/27/2019

Kernel Stein Tests for Multiple Model Comparison

We address the problem of non-parametric multiple model comparison: give...

16 Jen Ning Lim, et al. ∙

research

∙ 10/14/2019

More Powerful Selective Kernel Tests for Feature Selection

Refining one's hypotheses in the light of data is a commonplace scientif...

12 Jen Ning Lim, et al. ∙

research

∙ 10/10/2019

On Scalable Variant of Wasserstein Barycenter

We study a variant of Wasserstein barycenter problem, which we refer to ...

8 Tam Le, et al. ∙

research

∙ 10/10/2019

Computationally Efficient Tree Variants of Gromov-Wasserstein

We propose two novel variants of Gromov-Wasserstein (GW) between probabi...

3 Tam Le, et al. ∙

research

∙ 09/05/2019

LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport

Estimating mutual information is an important machine learning and stati...

6 Yanbin Liu, et al. ∙

research

∙ 08/30/2019

Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel

Transformer is a powerful architecture that achieves superior performanc...

32 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 05/24/2019

Approximation Ratios of Graph Neural Networks for Combinatorial Problems

In this paper, from a theoretical perspective, we study how powerful gra...

5 Ryoma Sato, et al. ∙

research

∙ 02/26/2019

Topological Bayesian Optimization with Persistence Diagrams

Finding an optimal parameter of a black-box function is important for se...

10 Tatsuya Shiraishi, et al. ∙

research

∙ 02/26/2019

Learning to Find Hard Instances of Graph Problems

Finding hard instances, which need a long time to solve, of graph proble...

8 Ryoma Sato, et al. ∙

research

∙ 02/01/2019

Tree-Sliced Approximation of Wasserstein Distances

Optimal transport () theory provides a useful set of tools to compare pr...

12 Tam Le, et al. ∙

research

∙ 01/23/2019

Constant Time Graph Neural Networks

Recent advancements in graph neural networks (GNN) have led to state-of-...

12 Ryoma Sato, et al. ∙

research

∙ 02/17/2018

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator

Measuring divergence between two distributions is essential in machine l...

0 Makoto Yamada, et al. ∙

research

∙ 02/15/2018

Selecting the Best in GANs Family: a Post Selection Inference Framework

"Which Generative Adversarial Networks (GANs) generates the most plausib...

0 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 02/15/2018

"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study

Recent works investigated the generalization properties in deep neural n...

0 Denny Wu, et al. ∙

research

∙ 02/10/2018

Riemannian Manifold Kernel for Persistence Diagrams

Algebraic topology methods have recently played an important role for st...

0 Tam Le, et al. ∙

research

∙ 11/16/2017

Deep Matching Autoencoders

Increasingly many real world tasks involve data in multiple modalities o...

0 Tanmoy Mukherjee, et al. ∙

research

∙ 05/15/2017

Convex Coupled Matrix and Tensor Completion

We propose a set of convex low rank inducing norms for a coupled matrice...

0 Kishan Wimalawarne, et al. ∙

research

∙ 02/21/2017

Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation

We propose an inlier-based outlier detection method capable of both iden...

0 Makoto Yamada, et al. ∙

research

∙ 10/12/2016

Post Selection Inference with Kernels

We propose a novel kernel based post selection inference (PSI) algorithm...

0 Makoto Yamada, et al. ∙

Makoto Yamada

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