Our paper proposing a method for integrating responses collected by crowdsourcing when observation bias exists has been accepted by ICPR 2022.

Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima.
Mitigating Observation Biases in Crowdsourced Label Aggregation.
In Proceedings of the 26th International Conference on Pattern Recognition (ICPR), 2022.

A paper proposing a new feature selection method for intervention effect estimation has been accepted by UAI 2022.

Yoichi Chikahara, Makoto Yamada, Hisashi Kashima.
Feature Selection for Discovering Distributional Treatment Effect Modifiers.
In Proceedings of the 38th Conference on Uncertaintly in Artificial Intelligence (UAI), 2022.

A paper proposing that a simple bag-of-words can perform close to Word Mover’s Distance by using proper normalization has been accepted by ICML 2022he international conference on machine learning.

Ryoma Sato, Makoto Yamada, Hisashi Kashima.
Re-evaluating Word Mover’s Distance.
In Proceedings of the 39th International Conference on Machine Learning (ICML), 2022.

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.

A paper was accepted for Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) 2022:

• Ryoma Sato. Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 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 AISTATS 2022

• Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada
  Fixed Support Tree-Sliced Wasserstein Barycenter
  In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022)
  
• Benjamin Poignard, Peter Naylor, Héctor Climente, Makoto Yamada
  Feature Screening with Kernel Knockoff
  In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 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 knowledge tracing with extracting interpretable feature trees was accepted for AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI):

Sein Minn, Jill-Jenn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu. Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations. In Proceedings of the 12th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI), 2022.

A paper on discriminant dynamic mode decomposition was accepted for publication in SIAM Journal on Applied Dynamical Systems (SIADS) : Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, and Yoshinobu Kawahara.
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections.
SIAM Journal on Applied Dynamical Systems (SIADS), 2021.

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.

Two papers were accepted for International Conference on Web Search and Data Mining (WSDM) 2022:

• Ryoma Sato.
  Enumerating Fair Packages for Group Recommendations.
  In Proceedings of the Fifteenth International Conference on Web Search and Data Mining (WSDM), 2022.
• Ryoma Sato.
  Retrieving Black-box Optimal Images from External Databases.
  In Proceedings of the Fifteenth International Conference on Web Search and Data Mining (WSDM), 2022.

Two papers were accepted for Advances in Neural Information Processing Systems (NeurIPS) 2021!

• Hiroaki Yamada, Makoto Yamada.
  Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares
  Advances in Neural Information Processing Systems (NeurIPS), 2021
• Tam Le, Truyen Nguyen, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen.
  Adversarial Regression with Doubly Non-negative Weighting Matrices
  Advances in Neural Information Processing Systems (NeurIPS), 2021

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 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).