Naoto Ohsaka

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 57

Top Topics

Influence Maximization

Collaborative Filtering

Point Processes

Top Venues

Electron. Colloquium Comput. Complex.

Publications

Naoto Ohsaka
Gap Amplification for Reconfiguration Problems. SODA (2024)
Naoto Ohsaka
Alphabet Reduction for Reconfiguration Problems. ICALP (2024)
Shuichi Hirahara, Naoto Ohsaka
Probabilistically Checkable Reconfiguration Proofs and Inapproximability of Reconfiguration Problems. Electron. Colloquium Comput. Complex. (2024)
Naoto Ohsaka
Alphabet Reduction for Reconfiguration Problems. CoRR (2024)
Naoto Ohsaka
Tight Inapproximability of Target Set Reconfiguration. CoRR (2024)
Shuichi Hirahara, Naoto Ohsaka
Optimal PSPACE-hardness of Approximating Set Cover Reconfiguration. Electron. Colloquium Comput. Complex. (2024)
Shuichi Hirahara, Naoto Ohsaka
Optimal PSPACE-hardness of Approximating Set Cover Reconfiguration. CoRR (2024)
Shuichi Hirahara, Naoto Ohsaka
Optimal PSPACE-Hardness of Approximating Set Cover Reconfiguration. ICALP (2024)
Shuichi Hirahara, Naoto Ohsaka
Probabilistically Checkable Reconfiguration Proofs and Inapproximability of Reconfiguration Problems. CoRR (2024)
Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura
Safe Collaborative Filtering. ICLR (2024)
Naoto Ohsaka
On the Parameterized Intractability of Determinant Maximization. Algorithmica 86 (6) (2024)
Shuichi Hirahara, Naoto Ohsaka
Probabilistically Checkable Reconfiguration Proofs and Inapproximability of Reconfiguration Problems. STOC (2024)
Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu
Matroid Semi-Bandits in Sublinear Time. CoRR (2024)
Tatsuya Matsuoka, Naoto Ohsaka
Computational complexity of normalizing constants for the product of determinantal point processes. Theor. Comput. Sci. 997 (2024)
Naoto Ohsaka
On reconfigurability of target sets. Theor. Comput. Sci. 942 (2023)
Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura
Safe Collaborative Filtering. CoRR (2023)
Naoto Ohsaka
Gap Amplification for Reconfiguration Problems. CoRR (2023)
Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. CoRR (2023)
Naoto Ohsaka, Riku Togashi
A Critical Reexamination of Intra-List Distance and Dispersion. CoRR (2023)
Naoto Ohsaka
Gap Preserving Reductions Between Reconfiguration Problems. STACS (2023)
Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. RecSys (2023)
Naoto Ohsaka, Riku Togashi
Curse of "Low" Dimensionality in Recommender Systems. CoRR (2023)
Naoto Ohsaka, Riku Togashi
A Critical Reexamination of Intra-List Distance and Dispersion. SIGIR (2023)
Naoto Ohsaka
On Approximate Reconfigurability of Label Cover. CoRR (2023)
Naoto Ohsaka, Riku Togashi
Curse of "Low" Dimensionality in Recommender Systems. SIGIR (2023)
Naoto Ohsaka
Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes. J. Artif. Intell. Res. 73 (2022)
Naoto Ohsaka
On the Parameterized Intractability of Determinant Maximization. CoRR (2022)
Naoto Ohsaka
On the Parameterized Intractability of Determinant Maximization. ISAAC (2022)
Naoto Ohsaka
Gap Preserving Reductions between Reconfiguration Problems. CoRR (2022)
Naoto Ohsaka, Tatsuya Matsuoka
Reconfiguration Problems on Submodular Functions. WSDM (2022)
Naoto Ohsaka
A Fully Polynomial Parameterized Algorithm for Counting the Number of Reachable Vertices in a Digraph. CoRR (2021)
Naoto Ohsaka
On Reconfigurability of Target Sets. CoRR (2021)
Tatsuya Matsuoka, Naoto Ohsaka
Maximization of Monotone k-Submodular Functions with Bounded Curvature and Non-k-Submodular Functions. ACML (2021)
Tatsuya Matsuoka, Naoto Ohsaka, Akihiro Yabe
On the Convex Combination of Determinantal Point Processes. ACML (2021)
Tatsuya Matsuoka, Naoto Ohsaka
Spanning Tree Constrained Determinantal Point Processes are Hard to (Approximately) Evaluate. CoRR (2021)
Naoto Ohsaka, Tatsuya Matsuoka
Reconfiguration Problems on Submodular Functions. CoRR (2021)
Naoto Ohsaka, Tatsuya Matsuoka
Computational Complexity of Normalizing Constants for the Product of Determinantal Point Processes. CoRR (2021)
Tomoya Sakai, Naoto Ohsaka
Predictive Optimization with Zero-Shot Domain Adaptation. SDM (2021)
Naoto Ohsaka, Tatsuya Matsuoka
Approximation algorithm for submodular maximization under submodular cover. UAI (2021)
Naoto Ohsaka
Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes. CoRR (2021)
Naoto Ohsaka
A fully polynomial parameterized algorithm for counting the number of reachable vertices in a digraph. Inf. Process. Lett. 171 (2021)
Naoto Ohsaka
Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability. AISTATS (2021)
Tomoya Sakai, Naoto Ohsaka
Predictive Optimization with Zero-Shot Domain Adaptation. CoRR (2021)
Tatsuya Matsuoka, Shinji Ito, Naoto Ohsaka
Tracking Regret Bounds for Online Submodular Optimization. AISTATS (2021)
Tatsuya Matsuoka, Naoto Ohsaka
Spanning tree constrained determinantal point processes are hard to (approximately) evaluate. Oper. Res. Lett. 49 (3) (2021)
Naoto Ohsaka, Tatsuya Matsuoka
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes. ICML (2020)
Naoto Ohsaka
The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches. SIGMOD Conference (2020)
Naoto Ohsaka, Tomoya Sakai, Akihiro Yabe
A Predictive Optimization Framework for Hierarchical Demand Matching. SDM (2020)
Naoto Ohsaka
The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches. CoRR (2020)
Yoichi Iwata, Tomoaki Ogasawara, Naoto Ohsaka
On the Power of Tree-Depth for Fully Polynomial FPT Algorithms. STACS (2018)
Diana Popova, Naoto Ohsaka, Ken-ichi Kawarabayashi, Alex Thomo
NoSingles: a space-efficient algorithm for influence maximization. SSDBM (2018)
Naoto Ohsaka, Tomohiro Sonobe, Naonori Kakimura, Takuro Fukunaga, Sumio Fujita, Ken-ichi Kawarabayashi
Boosting PageRank Scores by Optimizing Internal Link Structure. DEXA (1) (2018)
Naoto Ohsaka, Tomohiro Sonobe, Sumio Fujita, Ken-ichi Kawarabayashi
Coarsening Massive Influence Networks for Scalable Diffusion Analysis. SIGMOD Conference (2017)
Naoto Ohsaka, Yuichi Yoshida
Portfolio Optimization for Influence Spread. WWW (2017)
Yoichi Iwata, Tomoaki Ogasawara, Naoto Ohsaka
On the Power of Tree-Depth for Fully Polynomial FPT Algorithms. CoRR (2017)
Naoto Ohsaka, Yutaro Yamaguchi, Naonori Kakimura, Ken-ichi Kawarabayashi
Maximizing Time-Decaying Influence in Social Networks. ECML/PKDD (1) (2016)
Naoto Ohsaka, Takuya Akiba, Yuichi Yoshida, Ken-ichi Kawarabayashi
Dynamic Influence Analysis in Evolving Networks. Proc. VLDB Endow. 9 (12) (2016)
Naoto Ohsaka, Yuichi Yoshida
Monotone k-Submodular Function Maximization with Size Constraints. NIPS (2015)
Naoto Ohsaka, Takanori Maehara, Ken-ichi Kawarabayashi
Efficient PageRank Tracking in Evolving Networks. KDD (2015)
Naoto Ohsaka, Takuya Akiba, Yuichi Yoshida, Ken-ichi Kawarabayashi
Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations. AAAI (2014)
Naoto Ohsaka, Daisuke Kitakoshi, Masato Suzuki
A reinforcement learning method to improve the sweeping efficiency for an agent. GrC (2011)