LATEST NEWS

2022-11-22
Paper AAAI-23 "Wasserstein Graph Distance with L1 TED between WL Subtrees" NEW!
2022-07-18
News NMFLibrary 2.0 Release
2022-07-12
Paper arXiv paper "Wasserstein Graph Distance with L1 TED between WL Subtrees"
2022-05-31
Paper arXiv paper "On the convergence of semi-relaxed Sinkhorn algorithm"
2022-05-17
Paper EUSIPCO2022 "Auto-weighted sequential Wasserstein distance"
2022-05-10
News New members for FY2022 (3rd term)
2022-01-25
Paper ICASSP2022 "Block-coordinate Frank-Wolfe algorithm for semi-relaxed OT"
2021-10-22
News Book chapter in "Tensors for Data Processing (1st Edition)"
2021-08-05
Paper Journal Signal Processing "LCS graph kernel based on Wasserstein distance"
2021-04-21
Event New members for FY2021 (2nd term)
2021-03-11
Paper arXiv paper "Block-coordinate Frank-Wolfe for semi-relaxed optimal transport"
2021-03-03
Paper arXiv paper and tool "Manifold optimization for optimal transport"
2021-01-30
Paper ICASSP2021 "Graph embedding using multi-layer adjacent point merging model"
2020-12-11
Presen OPT2020 "Riemannian optimization on the simplex of positive definite matrices"
2020-10-12
Paper ICPR2020 "Wasserstein k-means with sparse simplex projection"
2020-06-03
Paper EUSIPCO2020 "Consistency- and inconsistency-aware graph-based multi-view clustering"
2020-04-26
Event New members for FY2020 (First term)
2020-04-15
Event Article for Close Up
2020-01-25
Paper ICASSP2020 three papers
2019-09-21
News Laboratory Open at Waseda University
2019-06-15
Presen Presentation at ICML2019
2019-02-07
Paper R-SVRG accepted to SIAM Journal on Optimization (SIOPT)
2018-12-25
Paper Grassmann Manifold Distributed Opt. accepted to Machine Learning
2018-12-17
Presen Presentation at NeurIPS2018

Optimization, machine learning, and signal processing

We are dedicated to optimization, machine learning, and signal processing, and their theories, algorithms, and applications for small- and large-scale data. Specifically, we address optimization algorithms,  linear & non-linear optimization problems, convex & non-convex optimization problems, classification and clustering problems, distance and space learning problems, structure learning, and optimal transport problems. Our focus includes the development and validation of optimization and learning algorithms, the establishment of new machine learning models, and those practical applications. For those items, we tackle with respect to theoretic approaches and numerical evaluations. Its applications range from data analysis, computer vision, video surveillance, network traffic analysis, distributed sensing, graph analysis, and so on.

RESEARCH TOPICS