Naoto Ohsaka
Naoto Ohsaka
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Cited by
Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations
N Ohsaka, T Akiba, Y Yoshida, K Kawarabayashi
Proceedings of the AAAI Conference on Artificial Intelligence, 138–144, 2014
Dynamic Influence Analysis in Evolving Networks
N Ohsaka, T Akiba, Y Yoshida, K Kawarabayashi
Proceedings of the VLDB Endowment 9 (12), 1077–1088, 2016
Efficient PageRank Tracking in Evolving Networks
N Ohsaka, T Maehara, K Kawarabayashi
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
Monotone k-Submodular Function Maximization with Size Constraints
N Ohsaka, Y Yoshida
Proceedings of the 29th Annual Conference on Neural Information Processing …, 2015
On the Power of Tree-Depth for Fully Polynomial FPT Algorithms
Y Iwata, T Ogasawara, N Ohsaka
Proceedings of the 35th International Symposium on Theoretical Aspects of …, 2018
Coarsening Massive Influence Networks for Scalable Diffusion Analysis
N Ohsaka, T Sonobe, S Fujita, K Kawarabayashi
Proceedings of the 2017 ACM SIGMODInternational Conference on Management of …, 2017
Portfolio Optimization for Influence Spread
N Ohsaka, Y Yoshida
Proceedings of the 26th International Conference on World Wide Web, 977–985, 2017
Maximizing Time-Decaying Influence in Social Networks
N Ohsaka, Y Yamaguchi, N Kakimura, K Kawarabayashi
Proceedings of the 15th European Conference on Machine Learning and …, 2016
NoSingles: A Space-Efficient Algorithm for Influence Maximization
D Popova, N Ohsaka, K Kawarabayashi, A Thomo
Proceedings of the 30th International Conference on Scientific and …, 2018
The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches
N Ohsaka
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
N Ohsaka, T Matsuoka
Proceedings of the 37th International Conference on Machine Learning, 7414–7423, 2020
Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability
N Ohsaka
Proceedings of the 24th International Conference on Artificial Intelligence …, 2021
Predictive Optimization with Zero-Shot Domain Adaptation
T Sakai, N Ohsaka
Proceedings of the 2021 SIAM International Conference on Data Mining, 369–377, 2021
A Predictive Optimization Framework for Hierarchical Demand Matching
N Ohsaka, T Sakai, A Yabe
Proceedings of the 2020 SIAM International Conference on Data Mining, 172–180, 2020
Boosting PageRank Scores by Optimizing Internal Link Structure
N Ohsaka, T Sonobe, N Kakimura, T Fukunaga, S Fujita, ...
Proceedings of the 29th International Conference on Database and Expert …, 2018
A Reinforcement Learning Method to Improve the Sweeping Efficiency for an Agent
N Ohsaka, D Kitakoshi, M Suzuki
Proceedings of the 2011 IEEE International Conference on Granular Computing …, 2011
Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes
N Ohsaka
arXiv preprint arXiv:2109.00727, 2021
On Reconfigurability of Target Sets
N Ohsaka
arXiv preprint arXiv:2107.09885, 2021
A Fully Polynomial Parameterized Algorithm for Counting the Number of Reachable Vertices in a Digraph
N Ohsaka
Information Processing Letters 171, 106137, 2021
Spanning Tree Constrained Determinantal Point Processes are Hard to (Approximately) Evaluate
T Matsuoka, N Ohsaka
Operations Research Letters 49 (3), 304–309, 2021
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