| 2025 | CVPR | Instance-wise Supervision-level Optimization in Active Learning. | Shinnosuke Matsuo, Riku Togashi, Ryoma Bise, Seiichi Uchida, Masahiro Nomura |
| 2024 | ECCV | Robust Nearest Neighbors for Source-Free Domain Adaptation Under Class Distribution Shift. | Antonio Tejero-de-Pablos, Riku Togashi, Mayu Otani, Shin'ichi Satoh |
| 2024 | ICLR | Safe Collaborative Filtering. | Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura |
| 2024 | WWW | Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. | Riku Togashi, Kenshi Abe, Yuta Saito |
| 2023 | CVPR | Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation. | Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami, Yuta Nakashima, Esa Rahtu, Janne Heikkil, Shin'ichi Satoh |
| 2023 | RecSys | Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. | Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka |
| 2023 | SIGIR | Curse of "Low" Dimensionality in Recommender Systems. | Naoto Ohsaka, Riku Togashi |
| 2023 | SIGIR | A Critical Reexamination of Intra-List Distance and Dispersion. | Naoto Ohsaka, Riku Togashi |
| 2023 | SIGIR | Exploration of Unranked Items in Safe Online Learning to Re-Rank. | Hiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi |
| 2022 | CVPR | Optimal Correction Cost for Object Detection Evaluation. | Mayu Otani, Riku Togashi, Yuta Nakashima, Esa Rahtu, Janne Heikkil, Shin'ichi Satoh |
| 2022 | CVPR | AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval. | Riku Togashi, Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkil, Tetsuya Sakai |
| 2022 | RecSys | Matching Theory-based Recommender Systems in Online Dating. | Yoji Tomita, Riku Togashi, Daisuke Moriwaki |
| 2021 | WWW | Density-Ratio Based Personalised Ranking from Implicit Feedback. | Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi Satoh |
| 2021 | SIGIR | Scalable Personalised Item Ranking through Parametric Density Estimation. | Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh |
| 2021 | WSDM | Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph. | Riku Togashi, Mayu Otani, Shin'ichi Satoh |
| 2020 | SIGIR | Visual Intents vs. Clicks, Likes, and Purchases in E-commerce. | Riku Togashi, Tetsuya Sakai |
| 2019 | ICTAI | Uplift Modeling for Cost Effective Coupon Marketing in C-to-C E-Commerce. | Akihiro Shimizu, Riku Togashi, Antony Lam, Nam Van Huynh |
| 2019 | ICTIR | Generalising Kendall's Tau for Noisy and Incomplete Preference Judgements. | Riku Togashi, Tetsuya Sakai |
| 2019 | SIGIR | Closing the Gap Between Query and Database through Query Feature Transformation in C2C e-Commerce Visual Search. | Takuma Yamaguchi, Kosuke Arase, Riku Togashi, Shunya Ueta |
| 2018 | ICTIR | Classifying Community QA Questions That Contain an Image. | Kenta Tamaki, Riku Togashi, Sosuke Kato, Sumio Fujita, Hideyuki Maeda, Tetsuya Sakai |
| 2017 | IJCNLP | Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering. | Kaustubh Kulkarni, Riku Togashi, Hideyuki Maeda, Sumio Fujita |
| 2017 | WWW | Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling. | Vibhor Kanojia, Hideyuki Maeda, Riku Togashi, Sumio Fujita |
| 2017 | WWW | Euclidean Image Embedding in view of Similarity Ranking in Auction Search by Image. | Riku Togashi, Hideyuki Maeda, Vibhor Kanojia, Kousuke Morimoto, Sumio Fujita |
| 2017 | SIGIR | LSTM vs. BM25 for Open-domain QA: A Hands-on Comparison of Effectiveness and Efficiency. | Sosuke Kato, Riku Togashi, Hideyuki Maeda, Sumio Fujita, Tetsuya Sakai |