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ORCIDNaoto Ohsaka
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2020 – today
- 2024
[j7]Naoto Ohsaka:
On the Parameterized Intractability of Determinant Maximization. Algorithmica 86(6): 1731-1763 (2024)- [j6]Tatsuya Matsuoka, Naoto Ohsaka
:
Computational complexity of normalizing constants for the product of determinantal point processes. Theor. Comput. Sci. 997: 114513 (2024) - [c31]Shuichi Hirahara, Naoto Ohsaka:
Optimal PSPACE-Hardness of Approximating Set Cover Reconfiguration. ICALP 2024: 85:1-85:18 - [c30]Naoto Ohsaka:
Alphabet Reduction for Reconfiguration Problems. ICALP 2024: 113:1-113:17 - [c29]Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura:
Safe Collaborative Filtering. ICLR 2024 - [c28]Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu:
Matroid Semi-Bandits in Sublinear Time. ICML 2024 - [c27]Naoto Ohsaka:
Gap Amplification for Reconfiguration Problems. SODA 2024: 1345-1366 - [c26]Shuichi Hirahara, Naoto Ohsaka:
Probabilistically Checkable Reconfiguration Proofs and Inapproximability of Reconfiguration Problems. STOC 2024: 1435-1445 - [i25]Shuichi Hirahara, Naoto Ohsaka:
Probabilistically Checkable Reconfiguration Proofs and Inapproximability of Reconfiguration Problems. CoRR abs/2401.00474 (2024) - [i24]Naoto Ohsaka:
Alphabet Reduction for Reconfiguration Problems. CoRR abs/2402.10627 (2024) - [i23]Shuichi Hirahara, Naoto Ohsaka:
Optimal PSPACE-hardness of Approximating Set Cover Reconfiguration. CoRR abs/2402.12645 (2024) - [i22]Naoto Ohsaka:
Tight Inapproximability of Target Set Reconfiguration. CoRR abs/2402.15076 (2024) - [i21]Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu:
Matroid Semi-Bandits in Sublinear Time. CoRR abs/2405.17968 (2024) - [i20]Shuichi Hirahara, Naoto Ohsaka:
Asymptotic Inapproximability of Reconfiguration Problems: Maxmin k-Cut and Maxmin Ek-SAT. CoRR abs/2410.03416 (2024) - [i19]Shuichi Hirahara, Naoto Ohsaka:
Probabilistically Checkable Reconfiguration Proofs and Inapproximability of Reconfiguration Problems. Electron. Colloquium Comput. Complex. TR24 (2024) - [i18]Shuichi Hirahara, Naoto Ohsaka:
Optimal PSPACE-hardness of Approximating Set Cover Reconfiguration. Electron. Colloquium Comput. Complex. TR24 (2024) - 2023
- [j5]Naoto Ohsaka:
On reconfigurability of target sets. Theor. Comput. Sci. 942: 253-275 (2023) - [c25]Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka:
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. RecSys 2023: 12-23 - [c24]Naoto Ohsaka, Riku Togashi:
Curse of "Low" Dimensionality in Recommender Systems. SIGIR 2023: 537-547 - [c23]Naoto Ohsaka, Riku Togashi:
A Critical Reexamination of Intra-List Distance and Dispersion. SIGIR 2023: 1619-1628 - [c22]Naoto Ohsaka:
Gap Preserving Reductions Between Reconfiguration Problems. STACS 2023: 49:1-49:18 - [i17]Naoto Ohsaka:
On Approximate Reconfigurability of Label Cover. CoRR abs/2304.08746 (2023) - [i16]Naoto Ohsaka, Riku Togashi:
Curse of "Low" Dimensionality in Recommender Systems. CoRR abs/2305.13597 (2023) - [i15]Naoto Ohsaka, Riku Togashi:
A Critical Reexamination of Intra-List Distance and Dispersion. CoRR abs/2305.13801 (2023) - [i14]Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura:
Safe Collaborative Filtering. CoRR abs/2306.05292 (2023) - [i13]Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka:
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. CoRR abs/2306.09060 (2023) - [i12]Naoto Ohsaka:
Gap Amplification for Reconfiguration Problems. CoRR abs/2310.14160 (2023) - 2022
- [j4]Naoto Ohsaka:
Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes. J. Artif. Intell. Res. 73: 709-735 (2022) - [c21]Naoto Ohsaka:
On the Parameterized Intractability of Determinant Maximization. ISAAC 2022: 46:1-46:16 - [c20]Naoto Ohsaka, Tatsuya Matsuoka:
Reconfiguration Problems on Submodular Functions. WSDM 2022: 764-774 - [i11]Naoto Ohsaka:
On the Parameterized Intractability of Determinant Maximization. CoRR abs/2209.12519 (2022) - [i10]Naoto Ohsaka:
Gap Preserving Reductions between Reconfiguration Problems. CoRR abs/2212.04207 (2022) - 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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last updated on 2024-11-13 23:51 CET by the dblp team
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