A reinforcement learning method to improve the sweeping efficiency for an agent. - Naoto Ohsaka; Daisuke Kitakoshi; Masato Suzuki (GrC, 2011)

Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations. - Naoto Ohsaka; Takuya Akiba; Yuichi Yoshida; Ken-ichi Kawarabayashi (AAAI, 2014)

Efficient PageRank Tracking in Evolving Networks. - Naoto Ohsaka; Takanori Maehara; Ken-ichi Kawarabayashi (KDD, 2015)

Monotone k-Submodular Function Maximization with Size Constraints. - Naoto Ohsaka; Yuichi Yoshida (NIPS, 2015)

Maximizing Time-Decaying Influence in Social Networks. - Naoto Ohsaka; Yutaro Yamaguchi 001; Naonori Kakimura; Ken-ichi Kawarabayashi (ECML/PKDD (1), 2016)

Dynamic Influence Analysis in Evolving Networks. - Naoto Ohsaka; Takuya Akiba; Yuichi Yoshida; Ken-ichi Kawarabayashi (Proc. VLDB Endow., 2016)

Coarsening Massive Influence Networks for Scalable Diffusion Analysis. - Naoto Ohsaka; Tomohiro Sonobe; Sumio Fujita; Ken-ichi Kawarabayashi (SIGMOD Conference, 2017)

Portfolio Optimization for Influence Spread. - Naoto Ohsaka; Yuichi Yoshida (WWW, 2017)

Boosting PageRank Scores by Optimizing Internal Link Structure. - Naoto Ohsaka; Tomohiro Sonobe; Naonori Kakimura; Takuro Fukunaga; Sumio Fujita; Ken-ichi Kawarabayashi (DEXA (1), 2018)

NoSingles: a space-efficient algorithm for influence maximization. - Diana Popova; Naoto Ohsaka; Ken-ichi Kawarabayashi; Alex Thomo (SSDBM, 2018)

On the Power of Tree-Depth for Fully Polynomial FPT Algorithms. - Yoichi Iwata; Tomoaki Ogasawara; Naoto Ohsaka (STACS, 2018)

On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes. - Naoto Ohsaka; Tatsuya Matsuoka (ICML, 2020)

A Predictive Optimization Framework for Hierarchical Demand Matching. - Naoto Ohsaka; Tomoya Sakai 001; Akihiro Yabe (SDM, 2020)

The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches. - Naoto Ohsaka (SIGMOD Conference, 2020)

Maximization of Monotone k-Submodular Functions with Bounded Curvature and Non-k-Submodular Functions. - Tatsuya Matsuoka; Naoto Ohsaka (ACML, 2021)

On the Convex Combination of Determinantal Point Processes. - Tatsuya Matsuoka; Naoto Ohsaka; Akihiro Yabe (ACML, 2021)

Tracking Regret Bounds for Online Submodular Optimization. - Tatsuya Matsuoka; Shinji Ito; Naoto Ohsaka (AISTATS, 2021)

Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability. - Naoto Ohsaka (AISTATS, 2021)

Predictive Optimization with Zero-Shot Domain Adaptation. - Tomoya Sakai 001; Naoto Ohsaka (SDM, 2021)

Approximation algorithm for submodular maximization under submodular cover. - Naoto Ohsaka; Tatsuya Matsuoka (UAI, 2021)

A fully polynomial parameterized algorithm for counting the number of reachable vertices in a digraph. - Naoto Ohsaka (Inf. Process. Lett., 2021)

Spanning tree constrained determinantal point processes are hard to (approximately) evaluate. - Tatsuya Matsuoka; Naoto Ohsaka (Oper. Res. Lett., 2021)

On the Parameterized Intractability of Determinant Maximization. - Naoto Ohsaka (ISAAC, 2022)

Reconfiguration Problems on Submodular Functions. - Naoto Ohsaka; Tatsuya Matsuoka (WSDM, 2022)

Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes. - Naoto Ohsaka (J. Artif. Intell. Res., 2022)

Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. - Yoji Tomita; Riku Togashi; Yuriko Hashizume; Naoto Ohsaka (RecSys, 2023)

Curse of "Low" Dimensionality in Recommender Systems. - Naoto Ohsaka; Riku Togashi (SIGIR, 2023)

A Critical Reexamination of Intra-List Distance and Dispersion. - Naoto Ohsaka; Riku Togashi (SIGIR, 2023)

Gap Preserving Reductions Between Reconfiguration Problems. - Naoto Ohsaka (STACS, 2023)

On reconfigurability of target sets. - Naoto Ohsaka (Theor. Comput. Sci., 2023)

Optimal PSPACE-Hardness of Approximating Set Cover Reconfiguration. - Shuichi Hirahara; Naoto Ohsaka (ICALP, 2024)

Alphabet Reduction for Reconfiguration Problems. - Naoto Ohsaka (ICALP, 2024)

Safe Collaborative Filtering. - Riku Togashi; Tatsushi Oka; Naoto Ohsaka; Tetsuro Morimura (ICLR, 2024)

Matroid Semi-Bandits in Sublinear Time. - Ruo-Chun Tzeng; Naoto Ohsaka; Kaito Ariu (ICML, 2024)

Gap Amplification for Reconfiguration Problems. - Naoto Ohsaka (SODA, 2024)

Probabilistically Checkable Reconfiguration Proofs and Inapproximability of Reconfiguration Problems. - Shuichi Hirahara; Naoto Ohsaka (STOC, 2024)

Tight Inapproximability of Target Set Reconfiguration. - Naoto Ohsaka (CoRR, 2024)

Asymptotic Inapproximability of Reconfiguration Problems: Maxmin k-Cut and Maxmin Ek-SAT. - Shuichi Hirahara; Naoto Ohsaka (CoRR, 2024)

Computational complexity of normalizing constants for the product of determinantal point processes. - Tatsuya Matsuoka; Naoto Ohsaka (Theor. Comput. Sci., 2024)

Asymptotically Optimal Inapproximability of Maxmin k-Cut Reconfiguration. - Shuichi Hirahara; Naoto Ohsaka (ICALP, 2025)

Yet Another Simple Proof of the PCRP Theorem. - Naoto Ohsaka (ICALP, 2025)

Asymptotically Optimal Inapproximability of E$k$-SAT Reconfiguration. - Shuichi Hirahara; Naoto Ohsaka (Electron. Colloquium Comput. Complex., 2025)

On approximate reconfigurability of label cover. - Naoto Ohsaka (Inf. Process. Lett., 2025)

Asymptotically Optimal Inapproximability of Ek-SAT Reconfiguration. - Shuichi Hirahara; Naoto Ohsaka (CoRR, 2025)

Reachability of Independent Sets and Vertex Covers Under Extended Reconfiguration Rules. - Shuichi Hirahara; Naoto Ohsaka; Tatsuhiro Suga; Akira Suzuki 001; Yuma Tamura; Xiao Zhou 001 (CoRR, 2025)