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ORCIDNaoto Ohsaka
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2020 – today
- 2022
[j4]Naoto Ohsaka:
Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes. J. Artif. Intell. Res. 73: 709-735 (2022)- [c20]Naoto Ohsaka, Tatsuya Matsuoka:
Reconfiguration Problems on Submodular Functions. WSDM 2022: 764-774 - 2021
- [j3]Naoto Ohsaka
:
A fully polynomial parameterized algorithm for counting the number of reachable vertices in a digraph. Inf. Process. Lett. 171: 106137 (2021) - [j2]Tatsuya Matsuoka, Naoto Ohsaka:
Spanning tree constrained determinantal point processes are hard to (approximately) evaluate. Oper. Res. Lett. 49(3): 304-309 (2021) - [c19]Tatsuya Matsuoka, Naoto Ohsaka, Akihiro Yabe:
On the Convex Combination of Determinantal Point Processes. ACML 2021: 158-173 - [c18]Tatsuya Matsuoka, Naoto Ohsaka:
Maximization of Monotone k-Submodular Functions with Bounded Curvature and Non-k-Submodular Functions. ACML 2021: 1707-1722 - [c17]Naoto Ohsaka:
Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability. AISTATS 2021: 154-162 - [c16]Tatsuya Matsuoka, Shinji Ito, Naoto Ohsaka:
Tracking Regret Bounds for Online Submodular Optimization. AISTATS 2021: 3421-3429 - [c15]Tomoya Sakai, Naoto Ohsaka:
Predictive Optimization with Zero-Shot Domain Adaptation. SDM 2021: 369-377 - [c14]Naoto Ohsaka, Tatsuya Matsuoka:
Approximation algorithm for submodular maximization under submodular cover. UAI 2021: 792-801 - [i9]Tomoya Sakai, Naoto Ohsaka:
Predictive Optimization with Zero-Shot Domain Adaptation. CoRR abs/2101.06233 (2021) - [i8]Tatsuya Matsuoka, Naoto Ohsaka:
Spanning Tree Constrained Determinantal Point Processes are Hard to (Approximately) Evaluate. CoRR abs/2102.12646 (2021) - [i7]Naoto Ohsaka:
A Fully Polynomial Parameterized Algorithm for Counting the Number of Reachable Vertices in a Digraph. CoRR abs/2103.04595 (2021) - [i6]Naoto Ohsaka:
On Reconfigurability of Target Sets. CoRR abs/2107.09885 (2021) - [i5]Naoto Ohsaka:
Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes. CoRR abs/2109.00727 (2021) - [i4]Naoto Ohsaka, Tatsuya Matsuoka:
Reconfiguration Problems on Submodular Functions. CoRR abs/2111.14030 (2021) - [i3]Naoto Ohsaka, Tatsuya Matsuoka:
Computational Complexity of Normalizing Constants for the Product of Determinantal Point Processes. CoRR abs/2111.14148 (2021) - 2020
- [c13]Naoto Ohsaka, Tatsuya Matsuoka:
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes. ICML 2020: 7414-7423 - [c12]Naoto Ohsaka, Tomoya Sakai, Akihiro Yabe:
A Predictive Optimization Framework for Hierarchical Demand Matching. SDM 2020: 172-180 - [c11]Naoto Ohsaka:
The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches. SIGMOD Conference 2020: 2151-2166 - [i2]Naoto Ohsaka:
The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches. CoRR abs/2003.09816 (2020)
2010 – 2019
- 2018
- [c10]Naoto Ohsaka, Tomohiro Sonobe, Naonori Kakimura, Takuro Fukunaga, Sumio Fujita, Ken-ichi Kawarabayashi:
Boosting PageRank Scores by Optimizing Internal Link Structure. DEXA (1) 2018: 424-439 - [c9]Diana Popova, Naoto Ohsaka, Ken-ichi Kawarabayashi, Alex Thomo:
NoSingles: a space-efficient algorithm for influence maximization. SSDBM 2018: 18:1-18:12 - [c8]Yoichi Iwata, Tomoaki Ogasawara, Naoto Ohsaka:
On the Power of Tree-Depth for Fully Polynomial FPT Algorithms. STACS 2018: 41:1-41:14 - 2017
- [c7]Naoto Ohsaka, Tomohiro Sonobe, Sumio Fujita, Ken-ichi Kawarabayashi:
Coarsening Massive Influence Networks for Scalable Diffusion Analysis. SIGMOD Conference 2017: 635-650 - [c6]Naoto Ohsaka, Yuichi Yoshida:
Portfolio Optimization for Influence Spread. WWW 2017: 977-985 - [i1]Yoichi Iwata, Tomoaki Ogasawara, Naoto Ohsaka:
On the Power of Tree-Depth for Fully Polynomial FPT Algorithms. CoRR abs/1710.04376 (2017) - 2016
- [j1]Naoto Ohsaka, Takuya Akiba, Yuichi Yoshida, Ken-ichi Kawarabayashi:
Dynamic Influence Analysis in Evolving Networks. Proc. VLDB Endow. 9(12): 1077-1088 (2016) - [c5]Naoto Ohsaka, Yutaro Yamaguchi, Naonori Kakimura, Ken-ichi Kawarabayashi:
Maximizing Time-Decaying Influence in Social Networks. ECML/PKDD (1) 2016: 132-147 - 2015
- [c4]Naoto Ohsaka, Takanori Maehara, Ken-ichi Kawarabayashi:
Efficient PageRank Tracking in Evolving Networks. KDD 2015: 875-884 - [c3]Naoto Ohsaka, Yuichi Yoshida:
Monotone k-Submodular Function Maximization with Size Constraints. NIPS 2015: 694-702 - 2014
- [c2]Naoto Ohsaka, Takuya Akiba, Yuichi Yoshida, Ken-ichi Kawarabayashi:
Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations. AAAI 2014: 138-144 - 2011
- [c1]Naoto Ohsaka, Daisuke Kitakoshi, Masato Suzuki:
A reinforcement learning method to improve the sweeping efficiency for an agent. GrC 2011: 515-520
Coauthor Index
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