A Convex Formulation for Learning from Crowds. - Hiroshi Kajino; Yuta Tsuboi; Hisashi Kashima (AAAI, 2012)

Learning from Crowds and Experts. - Hiroshi Kajino; Yuta Tsuboi; Issei Sato; Hisashi Kashima (HCOMP@AAAI, 2012)

Clustering Crowds. - Hiroshi Kajino; Yuta Tsuboi; Hisashi Kashima (AAAI, 2013)

Quality Control for Crowdsourced Enumeration Tasks. - Shunsuke Kajimura; Yukino Baba; Hiroshi Kajino; Hisashi Kashima (HCOMP, 2014)

Instance-Privacy Preserving Crowdsourcing. - Hiroshi Kajino; Yukino Baba; Hisashi Kashima (HCOMP, 2014)

Preserving worker privacy in crowdsourcing. - Hiroshi Kajino; Hiromi Arai; Hisashi Kashima (Data Min. Knowl. Discov., 2014)

Quality Control for Crowdsourced POI Collection. - Shunsuke Kajimura; Yukino Baba; Hiroshi Kajino; Hisashi Kashima (PAKDD (2), 2015)

Active Learning for Multi-relational Data Construction. - Hiroshi Kajino; Akihiro Kishimoto; Adi Botea; Elizabeth M. Daly; Spyros Kotoulas (WWW, 2015)

Link Prediction by Incidence Matrix Factorization. - Sho Yokoi; Hiroshi Kajino; Hisashi Kashima (ECAI, 2016)

Bidirectional Learning for Time-series Models with Hidden Units. - Takayuki Osogami; Hiroshi Kajino; Taro Sekiyama (ICML, 2017)

A Functional Dynamic Boltzmann Machine. - Hiroshi Kajino (IJCAI, 2017)

Link Prediction in Sparse Networks by Incidence Matrix Factorization. - Sho Yokoi; Hiroshi Kajino; Hisashi Kashima (J. Inf. Process., 2017)

Neuron as an Agent. - Shohei Ohsawa; Kei Akuzawa; Tatsuya Matsushima; Gustavo Bezerra; Yusuke Iwasawa; Hiroshi Kajino; Seiya Takenaka; Yutaka Matsuo (ICLR (Workshop), 2018)

Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process. - Akifumi Wachi; Hiroshi Kajino; Asim Munawar (CoRR, 2018)

Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting. - Kohei Miyaguchi; Hiroshi Kajino (AAAI, 2019)

Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder. - Masataro Asai; Hiroshi Kajino (ICAPS, 2019)

Molecular Hypergraph Grammar with Its Application to Molecular Optimization. - Hiroshi Kajino (ICML, 2019)

A Differentiable Point Process with Its Application to Spiking Neural Networks. - Hiroshi Kajino (ICML, 2021)

Symbolic Reasoning in Latent Space: Classical Planning as an Example. - Masataro Asai; Hiroshi Kajino; Alex Fukunaga; Christian Muise (Neuro-Symbolic Artificial Intelligence, 2021)

Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology. - Hiroshi Kajino; Kohei Miyaguchi; Takayuki Osogami (CoRR, 2022)

Classical Planning in Deep Latent Space. - Masataro Asai; Hiroshi Kajino; Alex Fukunaga; Christian Muise (J. Artif. Intell. Res., 2022)

Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies. - Hiroshi Kajino; Kohei Miyaguchi; Takayuki Osogami (ICML, 2023)

MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network. - Akihiro Kishimoto; Hiroshi Kajino; Masataka Hirose; Junta Fuchiwaki; Indra Priyadarsini; Lisa Hamada; Hajime Shinohara; Daiju Nakano; Seiji Takeda (CoRR, 2023)

Improving Molecular Properties Prediction Through Latent Space Fusion. - Eduardo E. M. Soares; Akihiro Kishimoto; Emilio Vital Brazil; Seiji Takeda; Hiroshi Kajino; Renato Cerqueira (CoRR, 2023)