業績

国際会議/ワークショップ論文（査読あり）

• 1. Akihiro Kishimoto, Hiroshi Kajino, Masataka Hirose, Junta Fuchiwaki, Indra Priyadarsini, Lisa Hamada, Hajime Shinohara, Daiju Nakano, Seiji Takeda: "MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network", NeurIPS 2023 Workshop AI4Mat, 2023.

  2. Akihiro Kishimoto, Hiroshi Kajino, Hajime Shinohara, Daiju Nakano, Seiji Takeda: “Autoencoder based on Graph and Recurrent Neural Networks and Application to Property Prediction”, Materials Research Society (MRS) Fall Meeting, 2023.

  3. Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami: "Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies", ICML-23, Hawaii, 2023.

  4. Hiroshi Kajino: "A Differentiable Point Process with Its Application to Spiking Neural Networks", ICML-21, Virtual, 2021.

  5. Miguel Garcia-Ortegon, Andreas Bender, Carl E Rasmussen, Hiroshi Kajino, Sergio Bacallado, “Combining variational autoencoder representations with structural descriptors improves prediction of docking scores”, Machine Learning for Molecules Workshop @ NeurIPS 2020.

  6. Hiroshi Kajino: "Molecular Hypergraph Grammar with Its Application to Molecular Optimization", ICML-19, Long Beach, CA, 2019. [code]

  7. Masataro Asai, Hiroshi Kajino: "Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder", ICAPS-2019, Berkeley, CA, 2019.

  8. Kohei Miyaguchi, Hiroshi Kajino: "Cogra: Concept-drift-aware Stochastic Gradient Descent for Time-series Forecasting", In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), pp.xx-xx, Hawaii, USA, 2019.

  9. Shohei Ohsawa, Kei Akuzawa, Tatsuya Matsushima, Gustavo Bezerra, Yusuke Iwasawa, Hiroshi Kajino, Seiya Takenaka, Yutaka Matsuo: Neuron as an Agent, Workshops of the 6th International Conference on Learning Representations (ICLR-18 workshop), Vancouver, BC, Canada, 2018 [paper]

  10. Takayuki Osogami, Hiroshi Kajino, Taro Sekiyama: Bidirectional learning for time-series models with hidden units, In Proceedings of the Thirty-fourth International Conference on Machine Learning (ICML-2017), pp.2711-2720, Sydney Australia, 2017. [paper]

  11. Hiroshi Kajino: A Functional Dynamic Boltzmann Machine, In Proceedings of the International Joint Conference on Artificial Intelligence 2017 (IJCAI-2017), pp.1987-1993, Melbourne, Australia, 2017. [paper]

      • Errata 1: W^{[\delta]} and U_l are in R^{N \times N}, not in R^{N \times M}.

      • Errata 2: K(P, X^{[t]}) -> K_{\sigma^2}(P, X^{[t]}), the first term of the gradient of L^{[t]}(\Theta) w.r.t. b (although, this will not be problematic almost surely...).

  12. Sho Yokoi, Hiroshi Kajino, Hisashi Kashima: Link Prediction by Incidence Matrix Factorization, In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI), pp.xx-xx, The Hague, Holland, 2016.

  13. Shunsuke Kajimura, Yukino Baba, Hiroshi Kajino, Hisashi Kashima: Quality Control for Crowdsourced POI Collection, In Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.255-267, Ho Chi Minh City, Viet Nam, 2015. Long paper (27/405). [paper]

  14. Hiroshi Kajino, Akihiro Kishimoto, Adi Botea, Elizabeth Daly, Spyros Kotoulas: Active Learning for Multi-relational Data Construction, In Proceedings of the 24th International World Wide Web Conference (WWW 2015), pp.560-569, Florence, Italy, 2015. Acceptance rate = 131/929 = 14.1%. [paper] [slide]

      • Errata: The degree of freedom of a rotation matrix is d(d-1)/2, not (d-1).

  15. Hiroshi Kajino, Yukino Baba, Hisashi Kashima: Instance-privacy Preserving Crowdsourcing, In Proceedings of the Second AAAI Conference on Human Computation and Crowdsourcing (HCOMP-2014), pp. 96-103, Pittsburgh, USA, 2014. Acceptance rate = 26/80 = 32%. [paper] [slide] [code]

  16. Hiroshi Kajino, Yuta Tsuboi, Hisashi Kashima: Clustering Crowds, In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI), pp.1120-1127, Bellevue, Washington, USA, 2013. acceptance rate = 203/690 = 29%. [paper] [slide] [poster]

  17. Hiroshi Kajino, Yuta Tsuboi, Issei Sato, Hisashi Kashima: Learning from Crowds and Experts, In Proceedings of the 4th Human Computation Workshop, pp.107-113, Toronto, Ontario, Canada, 2012. [paper] [poster]

  18. Hiroshi Kajino, Yuta Tsuboi, Hisashi Kashima: A Convex Formulation for Learning from Crowds, In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI), pp.73-79, Toronto, Ontario, Canada, 2012. acceptance rate = 294/1129 = 26% [paper] [poster] [spotlight]