Our paper on Bayesian optimization where only subsets of variables can be specified was accepted for Machine Learning Journal.

Shogo Hayashi, Junya Honda, Hisashi Kashima.
Bayesian Optimization with Partially Specified Queries.
Machine Learning, 2021.

Our paper on analyzing trajectory data (such as GPS) is accepted for SIGSPATIAL 2021.
Koh Takeuchi, Masaaki Imaizumi, Shunsuke Kanda, Keisuke Fujii, Masakazu Ishihata, Takuya Maekawa, Ken Yoda, Yasuo Tabei.
Fréchet Kernel for Trajectory Data Analysis.
In Proceedings of 29th ACM International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2021).

Our paper on estimation of graph-structured treatments (such as chemical compounds) is accepted for CIKM 2021.

Shonosuke Harada, Hisashi Kashima.
GraphITE: Estimating Individual Effects of Graph-structured Treatments.
In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.

Our paper on a method for machine failure diagnosis using both software log and sensor data was accepted for ICECIE, a international conference on industrial engineering.

Takako Onishi, Hisashi Kashima.
Machine Failure Diagnosis by Combining Software Log and Sensor Data.
In Proceedings of IEEE International Conference on Electrical, Control and Instrumentation Engineering (ICECIE), 2021.

Three papers were accepted for ECML PKDD, a premier conference on machine learning and data mining:

• Lu Xiaotian, Arseny Tolmachev, Tatsuya Yamamoto, Koh Takeuchi, Seiji Okajima, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima.
  Crowdsourcing Evaluation of Saliency-based XAI Methods.
  In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021.
• Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima.
  Inter-domain Multi-relational Link Prediction.
  In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021.
• Yanbin Liu, Makoto Yamada, Yao-Hung Hubert Tsai, Tam Le, Ruslan Salakhutdinov, Yi Yang.
  LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport.
  In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021.

Our paper on machine learning approach for detecting attacks to in-vehicle network across different car models was accepted for IEEE ITSC 2021, a premier international conference on intelligent transportation systems:
Shu Nakamura, Koh Takeuchi, Hisashi Kashima, Takeshi Kishikawa, Takashi Ushio, Tomoyuki Haga, Takamitsu Sasaki .
In-Vehicle Network Attack Detection Across Vehicle Models: A Supervised-Unsupervised Hybrid Approach.
IEEE International Intelligent Transportation Systems Conference (ITSC), 2021.

Our paper on modeling spatio-temporal diffusion events and its application to event prediction was accepted for SIGKDD 2021.

Maya Okawa, Tomoharu Iwata, Yusuke Tanaka, Hiroyuki Toda, Takeshi Kurashima, Hisashi Kashima.
Dynamic Hawkes Processes for Discovering Time-evolving Communities’ States behind Diffusion Processes.
In Proceedings of the 27st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

Three papers were accepted for International Conference on Machine Learning (ICML 2021)!

• Yuki Takezawa, Ryoma Sato, Makoto Yamada
  Supervised Tree-Wasserstein Distance.
  In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.
• Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada
  Post-selection inference with HSIC-Lasso.
  In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.
• Vu Nguyen, Tam Le, Makoto Yamada, Michael A Osborne
  Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search.
  In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.

Our paper on estimation of causal effects of combinatorial treatments was accepted for PAKDD 2021:
Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima.
Causal Combinatorial Factorization Machines for Set-wise Recommendation.
In Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2021.

A paper titled “Combinatorial Q-Learning for Condition-based Infrastructure Maintenance” has been accepted for publication in IEEE Access, 2021.

Akira Tanimoto.
Combinatorial Q-Learning for Condition-based Infrastructure Maintenance.
IEEE Access, 2021.

Three papers were accepted for International Conference on Artificial Intelligence and Statistics (AISTATS).

• Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima.
  Regret Minimization for Causal Inference on Large Treatment Space.

• Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima.
  Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint.

• Tam Le, Nhat Ho, Makoto Yamada.
  Flow-based Alignment Approaches for Probability Measures in Different Spaces.

Our paper proposing the use of random features to increase the representation power of GNNs and its strong theoretical guarantees was accepted for SIAM Conference on Data Mining Conference (SDM 2021).

Ryoma Sato, Makoto Yamada, Hisashi Kashima.
Random Features Strengthen Graph Neural Networks.
In Proceedings of SIAM International Conference on Data Mining (SDM), 2021.

A paper proposing causal inference techniques for predicting guidance effects on crowd movements was accepted for AAMAS 2021:
Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi.
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference.
In Proceedings of 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021.

Two papers accepted to NeurIPS 2020

• Ryoma Sato, Makoto Yamada, Hisashi Kashima.
  Fast Unbalanced Optimal Transport on a Tree.
  Advances in Neural Information Processing Systems (NeurIPS 2020).
• Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov.
  Neural Methods for Point-wise Dependency Estimation
  Advances in Neural Information Processing Systems (NeurIPS 2020).

A paper proposing a method to fast and memory-efficient similarity search of massive spatial trajectories was accepted for SIGSPATIAL 2020:
Shunsuke Kanda, Koh Takeuchi, Keisuke Fujii, Yasuo Tabei.
Succinct Trit-array Trie for Scalable Trajectory Similarity Search.
In Proceedings of 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2020), 2020.

Two papers were accepted for DSAA 2020:

Luu Huu Phuc, Koh Takeuchi, Makoto Yamada, Hisashi Kashima.
Simultaneous Link Prediction on Unaligned Networks Using Graph Embedding and Optimal Transport.
In Proceedings of the the 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2020.

Hitoshi Kusano, Yuji Horiguchi, Yukino Baba and Hisashi Kashima.
Stress Prediction from Head Motion.
In Proceedings of the the 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2020.

A paper proposing a method to organize and prioritize many ideas using crowdsourcing was accepted for HCOMP 2020:
Yukino Baba, Jiyi Li, Hisashi Kashima.
CrowDEA: Multi-view Idea Prioritization with Crowds.
In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.

A paper on a semi-supervised estimation method for causal effect prediction was accepted for ECML PKDD:
Shounosuke Harada, Hisashi Kashima
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation.
In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2020.

A paper on fast deterministic algorithms for CUR matrix decomposition was accepted for International Conference on Machine Learning (ICML):
Yasutoshi Ida, Sekitoshi Kanai,Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance.
In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.

A paper on crowdsourcing aggregation is accepted for International Joint Conference on Artificial Intelligence (IJCAI):
Jiyi Li, Yasushi Kawase, Yukino Baba, Hisashi Kashima.
Performance as a Constraint: An Improved Wisdom of Crowds Using Performance Regularization.
In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020.