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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
IBISML |
2022-12-23 13:40 |
Kyoto |
Kyoto University (Primary: On-site, Secondary: Online) |
Multi-objective Bayesian Optimization for Identifying Distributionally-robust Pareto-frontier Yu Inatsu (Nitech), Ichiro Takeuchi (Nagoya Univ./RIKEN) IBISML2022-59 |
Pareto optimization is one of the multi-objective optimization problems for multiple black-box functions. Recently, an o... [more] |
IBISML2022-59 pp.112-119 |
IBISML |
2022-01-18 13:40 |
Online |
Online |
More Powerful Selective Inference for K-means clustering with Application to Single Cell Analysis Mizuki Sato, Yumehiro Omori, Yu Inatsu, Ichiro Takeuchi (NITech) IBISML2021-25 |
K-means clustering is the most famous clustering method because of its simplicity, and it has been applied to a wide ran... [more] |
IBISML2021-25 pp.54-60 |
PRMU |
2021-12-16 10:45 |
Online |
Online |
Selective Inference for Multi-dimensional Multiple Change-Points Ryota Sugiyama, Hiroki Toda (NIT), Vo Nguyen Le Duy (NIT/RIKEN), Yu Inatsu (NIT), Ichiro Takeuchi (NIT/RIKEN) PRMU2021-28 |
Detecting changes in the average structure of multi-dimensional sequence is an important task in various fields. Since c... [more] |
PRMU2021-28 pp.25-30 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2021-06-28 15:45 |
Online |
Online |
Active learning for distributionally robust chance-constrained optimization Yu Inatsu, Shion Takeno, Masayuki Karasuyama (Nitech), Ichiro Takeuchi (Nitech/RIKEN) NC2021-7 IBISML2021-7 |
Chance-constrained optimization (CCO) is one of the constrained optimization problems where some of the inputs to a blac... [more] |
NC2021-7 IBISML2021-7 pp.47-54 |
IBISML |
2021-03-02 10:25 |
Online |
Online |
Selective Inference for Convex Clustering Using Parametric Programming Yumehiro Omori, Yu Inatsu (Nitech), Ichiro Takeuchi (Nitech/RIKEN) IBISML2020-35 |
Traditional statistical inference assumes that the hypothesis is predetermined and cannot be used as is for statistical ... [more] |
IBISML2020-35 pp.9-15 |
IBISML |
2021-03-03 15:15 |
Online |
Online |
Selective Inference for Change-point Detection in Multi-dimensional Series Data Ryota Sugiyama, Hiroki Toda, Vo Nguyen Le Duy, Yu Inatsu (NIT), Ichiro Takeuchi (NIT/RIKEN) IBISML2020-51 |
Detecting changes of the average structures in a multi-dimensional sequence is an important task in various fields. In t... [more] |
IBISML2020-51 pp.63-70 |
IBISML |
2018-11-05 15:10 |
Hokkaido |
Hokkaido Citizens Activites Center (Kaderu 2.7) |
[Poster Presentation]
Active learning for identifying local minimum points based on the derivative of Gaussian process Yu Inatsu (RIKEN), Daisuke Sugita (NITech), Kazuaki Toyoura (Kyoto Univ.), Ichiro Takeuchi (NITech/RIKEN/NIMS) IBISML2018-94 |
In many fields such as materials science, knowing local minimum points of unknown functions is important for understand... [more] |
IBISML2018-94 pp.373-380 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
[Poster Presentation]
Correcting selection bias in active learning based on selective inference framework Yu Inatsu (RIKEN), Ichiro Takeuchi (Nitech/RIKEN/NIMS) IBISML2017-74 |
Consider the active learning that constructs regression model from given data and actually observes the value at the po... [more] |
IBISML2017-74 pp.289-296 |
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