Naoto Ohsaka

According to our database1, Naoto Ohsaka authored at least 26 papers between 2011 and 2022.

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Bibliography

2022
Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes.
J. Artif. Intell. Res., 2022

Reconfiguration Problems on Submodular Functions.
Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

2021
Spanning tree constrained determinantal point processes are hard to (approximately) evaluate.
Oper. Res. Lett., 2021

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

Computational Complexity of Normalizing Constants for the Product of Determinantal Point Processes.
CoRR, 2021

On Reconfigurability of Target Sets.
CoRR, 2021

Approximation algorithm for submodular maximization under submodular cover.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Predictive Optimization with Zero-Shot Domain Adaptation.
Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Tracking Regret Bounds for Online Submodular Optimization.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

On the Convex Combination of Determinantal Point Processes.
Proceedings of the Asian Conference on Machine Learning, 2021

Maximization of Monotone k-Submodular Functions with Bounded Curvature and Non-k-Submodular Functions.
Proceedings of the Asian Conference on Machine Learning, 2021

2020
The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches.
Proceedings of the 2020 International Conference on Management of Data, 2020

A Predictive Optimization Framework for Hierarchical Demand Matching.
Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes.
Proceedings of the 37th International Conference on Machine Learning, 2020

2018
On the Power of Tree-Depth for Fully Polynomial FPT Algorithms.
Proceedings of the 35th Symposium on Theoretical Aspects of Computer Science, 2018

NoSingles: a space-efficient algorithm for influence maximization.
Proceedings of the 30th International Conference on Scientific and Statistical Database Management, 2018

Boosting PageRank Scores by Optimizing Internal Link Structure.
Proceedings of the Database and Expert Systems Applications, 2018

2017
Portfolio Optimization for Influence Spread.
Proceedings of the 26th International Conference on World Wide Web, 2017

Coarsening Massive Influence Networks for Scalable Diffusion Analysis.
Proceedings of the 2017 ACM International Conference on Management of Data, 2017

2016
Dynamic Influence Analysis in Evolving Networks.
Proc. VLDB Endow., 2016

Maximizing Time-Decaying Influence in Social Networks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

2015
Monotone k-Submodular Function Maximization with Size Constraints.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Efficient PageRank Tracking in Evolving Networks.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014
Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2011
A reinforcement learning method to improve the sweeping efficiency for an agent.
Proceedings of the 2011 IEEE International Conference on Granular Computing, 2011


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