A paper proposing a method for predicting the physical properties of compounds by correcting the experimental biases has been accepted for publication in Scientific Reports.

Yang Liu, Hisashi Kashima.
Chemical Property Prediction Under Experimental Biases.
Scientific Reports, 2022.

Our paper on learning a classifier with near-miss (weak positive) labels was accepted for the journal of Expert Systems with Applications (ESWA).

Akira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima.
Improving Imbalanced Classification Using Near-miss Instances.
Expert Systems with Applications (ESWA), 2022.

Our paper on a paper submission recommendation system that takes into account authors’ submission bias has been accepted by Journal of Informetrics.

• Ryoma Sato, Makoto Yamada, Hisashi Kashima.
  Poincare: Recommending Publication Venues via Treatment Effect Estimation.
  Journal of Informetrics, 2022.

A paper proposing a method to predict spatio-temporal events with high accuracy by deepening the Hawkes process, a point process model, using CNN has been accepted for publication in Machine Learning (Special Issue of ECML PKDD).

Maya Okawa, Tomoharu Iwata, Yusuke Tanaka, Hiroyuki Toda, Takeshi Kurashima, Hisashi Kashima.
Context-aware Spatio-temporal Event Prediction via Convolutional Hawkes Processes.
Machine Learning, 2022.

Two papers were accepted for SIAM Inte

Ryoma Sato.
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?
In Proceedings of the SIAM International Conference on Data Mining (SDM), 2022.

Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima.
Learning Time-series Shapelets Enhancing Discriminability.
In Proceedings of the SIAM International Conference on Data Mining (SDM), 2022.

A paper on constant-time graph neural network was accepted for publication in ACM Transactions on Knowledge Discovery from Data (TKDD) :

Ryoma Sato, Makoto Yamada, Hisashi Kashima.
Constant Time Graph Neural Networks.
ACM Transactions on Knowledge Discovery from Data (TKDD), 2022. 

Our paper on predicting decisions by anesthesiologists (to administer analgesics) during surgery using machine learning has been accepted for publication in Scientific Reports. Naoki Miyaguchi, Koh Takeuchi, Hisashi Kashima, Mizuki Morita, Hiroshi Morimatsu.
Predicting Anesthetic Infusion Events Using Machine Learning.
Scientific Reports, 2022.

A paper on time series classification with time-varying shapelet (substring) pattern features was accepted for International Conference on Data Engineering (ICDE 2022): Akihiro Yamguchi, Ken Ueno, Hisashi Kashima.
Learning Evolvable Time-series Shapelets.
In Proceedings of the 38th International Conference on Data Engineering (ICDE), 2022.

Our paper on a method for integrating relative similarity comparison data between three objects collected by crowdsourcing has been accepted to ICONIP2021.

Jiyi Li, Lucas Ryo Endo, Hisashi Kashima.
Label Aggregation for Crowdsourced Triplet Similarity Comparisons.
In Proceedings of the 28th International Conference on Neural Information Processing (ICONIP), 2021.

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 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.

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.