Kaito Fujii

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I am an assistant professor at National Institute of Informatics.
I received my PhD from University of Tokyo where I was advised by Prof. Satoru Iwata.
Email: fujiik+at+nii.ac.jp
dblp google scholar

Research Interests

My research interests lie in the intersection of combinatorial optimization and machine learning.
I am particularly interested in submodular maximization, active learning, optimal stopping theory, online learning, and correlated equilibria.

Internship

I am looking for graduate students to collaborate with me through NII International Internship Program.
If you are interested, please do not hesitate to contact me via email. Please refer to these slides or these slides for more information about my research area.

Preprints

• Kaito Fujii
  Bayes correlated equilibria and no-regret dynamics
  ArXiv preprints, 2023.
  [slides]
• Kaito Fujii
  An improved algorithm for the submodular secretary problem with a cardinality constraint
  ArXiv preprints, 2019.

Refereed Publications

• Kaito Fujii and Yuichi Yoshida (alphabetical order)
  The secretary problem with predictions
  Mathematics of Operations Research, 49(2), pp. 1241--1262, 2023.
• Shinichi Hemmi, Taihei Oki, Shinsaku Sakaue, Kaito Fujii, and Satoru Iwata
  Lazy and fast greedy MAP inference for determinantal point process
  Advances in Neural Information Processing Systems (NeurIPS), 35, pp. 2776--2789, 2022.
  
• Thi Ha Ly Dinh, Megumi Kaneko, and Kaito Fujii
  Device selection and beamforming optimization in large-scale mmWave IoT networks
  IEEE Internet of Things Journal, 9(24), pp. 25395--25408, 2022.
  
• Tomohiro Nakamura, Shinsaku Sakaue, Kaito Fujii, Yu Harabuchi, Satoshi Maeda, and Satoru Iwata
  Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks
  Scientific Reports, 12, 1124, 2022.
• Kaito Fujii and Shinsaku Sakaue (alphabetical order)
  Algorithmic Bayesian persuasion with combinatorial actions
  Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), 36(5), pp. 5016--5024, 2022.
  [slides]
• Kaito Fujii, Tasuku Soma, and Yuichi Yoshida (alphabetical order)
  Polynomial-time algorithms for submodular Laplacian systems
  Theoretical Computer Science, 892(12), pp. 170--186, 2021.
• Kaito Fujii
  Approximation guarantees of local search algorithms via localizability of set functions
  Proceedings of the 37th International Conference on Machine Learning (ICML), pp. 3327--3336, 2020.
  [slides]
• Kaito Fujii and Shinsaku Sakaue
  Beyond adaptive submodularity: Approximation guarantees of greedy policy with adaptive submodularity ratio
  Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 2042--2051, 2019.
  [poster] [slides]
• Kaito Fujii and Tasuku Soma (alphabetical order)
  Fast greedy algorithms for dictionary selection with generalized sparsity constraints
  Advances in Neural Information Processing Systems (NeurIPS), 31, pp. 4745--4754, 2018. Spotlight (top 4% submissions).
  [poster] [spotlight slides]
• Kaito Fujii and Hisashi Kashima
  Budgeted stream-based active learning via adaptive submodular maximization
  Advances in Neural Information Processing Systems (NIPS), 29, pp. 514--522, 2016.
• Kaito Fujii
  Faster approximation algorithms for maximizing a monotone submodular function subject to a b-matching constraint
  Information Processing Letters, 116(9), pp. 578--584, 2016.