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Kudo, Kota
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1

Cutting-plane algorithm for estimation of sparse Cox propor..:

Saishu, Hiroki ; Kudo, Kota ; Takano, Yuichi
TOP.  , 2023
Link: https://doi.org/10.1007/..
 
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2

Sparse Poisson regression via mixed-integer optimization:

Saishu, Hiroki ; Kudo, Kota ; Takano, Yuichi.
PLOS ONE.  16 (2021)  4 - p. e0249916 , 2021
Link: https://doi.org/10.1371/..
 
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3

Stochastic Discrete First-Order Algorithm for Feature Subse..:

KUDO, Kota ; TAKANO, Yuichi ; NOMURA, Ryo
IEICE Transactions on Information and Systems.  E103.D (2020)  7 - p. 1693-1702 , 2020
Link: https://doi.org/10.1587/..
 
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4

Blood pressure measurement using piezoelectric effect by an..:

Arakawa, Mototaka ; Kudo, Kota ; Kobayashi, Kazuto.
Sensors and Actuators A: Physical.  286 (2019)  - p. 146-151 , 2019
Link: https://doi.org/10.1016/..
 
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5

Design and synthesis of biotinylated inositol 1,3,4,5-tetra..:

Anraku, Kensaku ; Inoue, Teruhiko ; Sugimoto, Kenji...
Bioorganic & Medicinal Chemistry.  19 (2011)  22 - p. 6833-6841 , 2011
Link: https://doi.org/10.1016/..
 
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6

$^{139}$La-NMR study of spin-dynamics coupled with hole mob..:

Taniguchi, Takanori ; Kitagawa, Shunsaku ; Ishida, Kenji...
http://arxiv.org/abs/2110.12333.  , 2021
Link: http://arxiv.org/abs/211..
 
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7

Fermi-liquid state in $T$*-type La$_{1-x/2}$Eu$_{1-x/2}$Sr$..:

Taniguchi, Takanori ; Kudo, Kota ; Asano, Shun...
http://arxiv.org/abs/2012.03529.  , 2020
Link: http://arxiv.org/abs/201..
 
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8

Distinct variation of electronic states due to annealing in..:

Asano, Shun ; Ishii, Kenji ; Matsumura, Daiju...
http://arxiv.org/abs/2005.10681.  , 2020
Link: http://arxiv.org/abs/200..
 
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9

Oxidation annealing effects on the spin-glass-like magnetis..:

Asano, Shun ; Suzuki, Kensuke M ; Kudo, Kota...
http://arxiv.org/abs/1904.07541.  , 2019
Link: http://arxiv.org/abs/190..
 
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10

Sparse Poisson regression via mixed-integer optimization:

Hiroki Saishu ; Kota Kudo ; Yuichi Takano
https://doi.org/10.1371/journal.pone.0249916.  , 2021
Link: https://doi.org/10.1371/..
 
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11

Results of our MIQO formulation for synthetic training inst..:

Hiroki Saishu (10687475) ; Kota Kudo (10687478) ; Yuichi Takano (3335556)
https://figshare.com/articles/dataset/Results_of_our_MIQO_formulation_for_synthetic_training_instances_i_i_5_/14468270.  , 2021
Link: https://doi.org/10.1371/..
 
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12

Prediction performance for synthetic test instances ( θ = 1..:

Hiroki Saishu (10687475) ; Kota Kudo (10687478) ; Yuichi Takano (3335556)
https://figshare.com/articles/dataset/Prediction_performance_for_synthetic_test_instances_i_i_10_/14468279.  , 2021
Link: https://doi.org/10.1371/..
 
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13

Prediction performance for real-world test instances:

Hiroki Saishu (10687475) ; Kota Kudo (10687478) ; Yuichi Takano (3335556)
https://figshare.com/articles/dataset/Prediction_performance_for_real-world_test_instances_/14468288.  , 2021
Link: https://doi.org/10.1371/..
 
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14

Real-world datasets:

Hiroki Saishu (10687475) ; Kota Kudo (10687478) ; Yuichi Takano (3335556)
https://figshare.com/articles/dataset/Real-world_datasets_/14468282.  , 2021
Link: https://doi.org/10.1371/..
 
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15

Results of our MIQO formulation for synthetic training inst..:

Hiroki Saishu (10687475) ; Kota Kudo (10687478) ; Yuichi Takano (3335556)
https://figshare.com/articles/dataset/Results_of_our_MIQO_formulation_for_synthetic_training_instances_i_i_10_/14468273.  , 2021
Link: https://doi.org/10.1371/..
 
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