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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 8 of 8  /   
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
 Results 1 - 8 of 8  /   
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