| 2026 | CHI | DiverXplorer: Stock Image Exploration via Diversity Adjustment for Graphic Design. | Antonio Tejero-de-Pablos, Sichao Song, Naoto Ohsaka, Mayu Otani, Shin'ichi Satoh |
| 2026 | ICALP | On (In)approximability of MaxMin Independent Set Reconfiguration. | Hung P. Hoang, Naoto Ohsaka, Rin Saito, Yuma Tamura |
| 2025 | FOCS | Asymptotically Optimal Inapproximability of Ek-SAT Reconfiguration. | Shuichi Hirahara, Naoto Ohsaka |
| 2025 | ICALP | Asymptotically Optimal Inapproximability of Maxmin k-Cut Reconfiguration. | Shuichi Hirahara, Naoto Ohsaka |
| 2025 | ICALP | Yet Another Simple Proof of the PCRP Theorem. | Naoto Ohsaka |
| 2025 | ISAAC | Reachability of Independent Sets and Vertex Covers Under Extended Reconfiguration Rules. | Shuichi Hirahara, Naoto Ohsaka, Tatsuhiro Suga, Akira Suzuki, Yuma Tamura, Xiao Zhou |
| 2024 | ICALP | Optimal PSPACE-Hardness of Approximating Set Cover Reconfiguration. | Shuichi Hirahara, Naoto Ohsaka |
| 2024 | ICALP | Alphabet Reduction for Reconfiguration Problems. | Naoto Ohsaka |
| 2024 | ICLR | Safe Collaborative Filtering. | Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura |
| 2024 | ICML | Matroid Semi-Bandits in Sublinear Time. | Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu |
| 2024 | SODA | Gap Amplification for Reconfiguration Problems. | Naoto Ohsaka |
| 2024 | STOC | Probabilistically Checkable Reconfiguration Proofs and Inapproximability of Reconfiguration Problems. | Shuichi Hirahara, Naoto Ohsaka |
| 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 | STACS | Gap Preserving Reductions Between Reconfiguration Problems. | Naoto Ohsaka |
| 2022 | ISAAC | On the Parameterized Intractability of Determinant Maximization. | Naoto Ohsaka |
| 2022 | WSDM | Reconfiguration Problems on Submodular Functions. | Naoto Ohsaka, Tatsuya Matsuoka |
| 2021 | ACML | Maximization of Monotone k-Submodular Functions with Bounded Curvature and Non-k-Submodular Functions. | Tatsuya Matsuoka, Naoto Ohsaka |
| 2021 | ACML | On the Convex Combination of Determinantal Point Processes. | Tatsuya Matsuoka, Naoto Ohsaka, Akihiro Yabe |
| 2021 | AISTATS | Tracking Regret Bounds for Online Submodular Optimization. | Tatsuya Matsuoka, Shinji Ito, Naoto Ohsaka |
| 2021 | AISTATS | Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability. | Naoto Ohsaka |
| 2021 | UAI | Approximation algorithm for submodular maximization under submodular cover. | Naoto Ohsaka, Tatsuya Matsuoka |
| 2021 | SDM | Predictive Optimization with Zero-Shot Domain Adaptation. | Tomoya Sakai, Naoto Ohsaka |
| 2020 | ICML | On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes. | Naoto Ohsaka, Tatsuya Matsuoka |
| 2020 | SIGMOD | The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches. | Naoto Ohsaka |
| 2020 | SDM | A Predictive Optimization Framework for Hierarchical Demand Matching. | Naoto Ohsaka, Tomoya Sakai, Akihiro Yabe |
| 2018 | DEXA | Boosting PageRank Scores by Optimizing Internal Link Structure. | Naoto Ohsaka, Tomohiro Sonobe, Naonori Kakimura, Takuro Fukunaga, Sumio Fujita, Ken-ichi Kawarabayashi |
| 2018 | STACS | On the Power of Tree-Depth for Fully Polynomial FPT Algorithms. | Yoichi Iwata, Tomoaki Ogasawara, Naoto Ohsaka |
| 2018 | SSDBM | NoSingles: a space-efficient algorithm for influence maximization. | Diana Popova, Naoto Ohsaka, Ken-ichi Kawarabayashi, Alex Thomo |
| 2017 | WWW | Portfolio Optimization for Influence Spread. | Naoto Ohsaka, Yuichi Yoshida |
| 2017 | SIGMOD | Coarsening Massive Influence Networks for Scalable Diffusion Analysis. | Naoto Ohsaka, Tomohiro Sonobe, Sumio Fujita, Ken-ichi Kawarabayashi |
| 2015 | KDD | Efficient PageRank Tracking in Evolving Networks. | Naoto Ohsaka, Takanori Maehara, Ken-ichi Kawarabayashi |
| 2014 | AAAI | Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations. | Naoto Ohsaka, Takuya Akiba, Yuichi Yoshida, Ken-ichi Kawarabayashi |
| 2011 | GRC | A reinforcement learning method to improve the sweeping efficiency for an agent. | Naoto Ohsaka, Daisuke Kitakoshi, Masato Suzuki |