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2020 – today
- 2024
[j21]Daichi Amagata
, Takahiro Hara:
Efficient Density-peaks Clustering Algorithms on Static and Dynamic Data in Euclidean Space. ACM Trans. Knowl. Discov. Data 18(1): 2:1-2:27 (2024)- [c60]Daichi Amagata:
Fair k-center Clustering with Outliers. AISTATS 2024: 10-18 - [c59]Ryo Shirai, Ryo Imai, Seng Pei Liew, Daichi Amagata, Tsubasa Takahashi, Takahiro Hara:
Estimating Visited Stores Through Positive-Unlabeled Learning. DASFAA (7) 2024: 377-389 - [c58]Daichi Amagata, Junya Yamada, Yuchen Ji, Takahiro Hara:
Efficient Algorithms for Top-k Stabbing Queries on Weighted Interval Data. DEXA (1) 2024: 146-152 - [c57]Yuchen Ji, Daichi Amagata, Yuya Sasaki, Takahiro Hara:
SAFE: Sampling-Assisted Fast Learned Cardinality Estimation for Dynamic Spatial Data. DEXA (2) 2024: 201-216 - [c56]Daichi Amagata:
Independent Range Sampling on Interval Data. ICDE 2024: 449-461 - [c55]Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, Mori Kurokawa:
Mutual Information-based Preference Disentangling and Transferring for Non-overlapped Multi-target Cross-domain Recommendations. SIGIR 2024: 2124-2133 - [i10]Daichi Amagata, Junya Yamada, Yuchen Ji, Takahiro Hara:
Efficient Algorithms for Top-k Stabbing Queries on Weighted Interval Data (Full Version). CoRR abs/2405.05601 (2024) - [i9]Daichi Amagata:
Independent Range Sampling on Interval Data (Longer Version). CoRR abs/2405.08315 (2024) - 2023
- [j20]Kohei Hirata, Daichi Amagata, Sumio Fujita, Takahiro Hara:
Categorical Diversity-Aware Inner Product Search. IEEE Access 11: 2586-2596 (2023) - [j19]Daichi Amagata, Takahiro Hara:
Reverse Maximum Inner Product Search: Formulation, Algorithms, and Analysis. ACM Trans. Web 17(4): 26:1-26:23 (2023) - [c54]Daichi Amagata:
Diversity Maximization in the Presence of Outliers. AAAI 2023: 12338-12345 - [c53]Keizo Hori, Yuya Sasaki, Daichi Amagata, Yuki Murosaki, Makoto Onizuka:
Learned Spatial Data Partitioning. aiDM@SIGMOD 2023: 3:1-3:8 - [c52]Hao Niu, Duc V. Nguyen, Kei Yonekawa, Mori Kurokawa, Chihiro Ono, Daichi Amagata, Takuya Maekawa, Takahiro Hara:
User-irrelevant Cross-domain Association Analysis for Cross-domain Recommendation with Transfer Learning. ICDAR@ICMR 2023: 37-45 - [c51]Shunya Nishio, Daichi Amagata, Takahiro Hara:
Lamps: Location-Aware Moving Top-k Pub/Sub (Extended abstract). ICDE 2023: 3809-3810 - [c50]Yue Zhao, Yihong Zhang, Daichi Amagata, Takahiro Hara:
POI Recommendation by Learning Short-, Long- and Mid-Term Preferences through GNN. MDM 2023: 1-10 - [c49]Shunya Nishio, Daichi Amagata, Takahiro Hara:
Approximate Reverse Top-k Spatial-Keyword Queries. MDM 2023: 96-105 - [c48]Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, Mori Kurokawa:
Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations. PAKDD (3) 2023: 271-283 - [c47]Kazuyoshi Aoyama, Daichi Amagata, Sumio Fujita, Takahiro Hara:
Simpler is Much Faster: Fair and Independent Inner Product Search. SIGIR 2023: 2379-2383 - [c46]Daichi Amagata, Alejandro Ramos, Ryo Shirai, Takahiro Hara:
Fast Algorithm for Embedded Order Dependency Validation. SSDBM 2023: 17:1-17:4 - [i8]Keizo Hori, Yuya Sasaki, Daichi Amagata, Yuki Murosaki, Makoto Onizuka:
Learned spatial data partitioning. CoRR abs/2306.04846 (2023) - [i7]Alejandro Ramos, Takuya Uemura, Daichi Amagata, Ryo Shirai, Takahiro Hara:
Fast Algorithm for Embedded Order Dependency Validation (Extended Version). CoRR abs/2312.16033 (2023) - 2022
- [j18]Ryosuke Taniguchi, Daichi Amagata, Takahiro Hara:
Efficient Retrieval of Top-k Weighted Triangles on Static and Dynamic Spatial Data. IEEE Access 10: 55298-55307 (2022) - [j17]Zhi Li, Daichi Amagata, Yihong Zhang, Takuya Maekawa, Takahiro Hara, Kei Yonekawa, Mori Kurokawa:
HML4Rec: Hierarchical meta-learning for cold-start recommendation in flash sale e-commerce. Knowl. Based Syst. 255: 109674 (2022) - [j16]Kohei Hirata, Daichi Amagata, Takahiro Hara:
Cardinality Estimation in Inner Product Space. IEEE Open J. Comput. Soc. 3: 208-216 (2022) - [j15]Shunya Nishio, Daichi Amagata, Takahiro Hara:
Lamps: Location-Aware Moving Top-k Pub/Sub. IEEE Trans. Knowl. Data Eng. 34(1): 352-364 (2022) - [j14]Daichi Amagata, Makoto Onizuka, Takahiro Hara:
Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces. VLDB J. 31(4): 797-821 (2022) - [c45]Daichi Amagata:
Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data. IEEE Big Data 2022: 445-454 - [c44]Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, Mori Kurokawa:
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations. IEEE Big Data 2022: 762-769 - [c43]Ryosuke Taniguchi, Daichi Amagata, Takahiro Hara:
Retrieving Top-N Weighted Spatial k-cliques. IEEE Big Data 2022: 4952-4961 - [c42]Ryosuke Taniguchi, Daichi Amagata, Takahiro Hara:
Efficient Retrieval of Top-k Weighted Spatial Triangles. DASFAA (1) 2022: 224-231 - [c41]Yuchen Ji, Daichi Amagata, Yuya Sasaki, Takahiro Hara:
A Performance Study of One-dimensional Learned Cardinality Estimation. DOLAP 2022: 86-90 - [c40]Daichi Amagata, Yusuke Arai, Sumio Fujita, Takahiro Hara:
Learned k-NN distance estimation. SIGSPATIAL/GIS 2022: 1:1-1:4 - [c39]Kohei Hirata, Daichi Amagata, Sumio Fujita, Takahiro Hara:
Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively. RecSys 2022: 198-207 - [i6]Daichi Amagata, Takahiro Hara:
Fast Density-Peaks Clustering: Multicore-based Parallelization Approach. CoRR abs/2207.04649 (2022) - [i5]Daichi Amagata, Yusuke Arai, Sumio Fujita, Takahiro Hara:
Learned k-NN Distance Estimation. CoRR abs/2208.14210 (2022) - [i4]Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, Mori Kurokawa:
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations. CoRR abs/2211.03390 (2022) - 2021
- [j13]Cheng Chen, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Predicting Next-use Mobile Apps Using App Semantic Representations. J. Inf. Process. 29: 597-609 (2021) - [c38]Hayato Nakama, Daichi Amagata, Takahiro Hara:
Approximate Top-k Inner Product Join with a Proximity Graph. IEEE BigData 2021: 4468-4471 - [c37]Yusuke Arai, Daichi Amagata, Sumio Fujita, Takahiro Hara:
LGTM: A Fast and Accurate kNN Search Algorithm in High-Dimensional Spaces. DEXA (2) 2021: 220-231 - [c36]Daichi Amagata, Shohei Tsuruoka, Yusuke Arai, Takahiro Hara:
Feat-SKSJ: Fast and Exact Algorithm for Top-k Spatial-Keyword Similarity Join. SIGSPATIAL/GIS 2021: 15-24 - [c35]Jun Murao, Kei Yonekawa, Mori Kurokawa, Daichi Amagata, Takuya Maekawa, Takahiro Hara:
Concept Drift Detection with Denoising Autoencoder in Incomplete Data. MobiQuitous 2021: 541-552 - [c34]Daichi Amagata, Takahiro Hara:
Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item? RecSys 2021: 273-281 - [c33]Daichi Amagata, Makoto Onizuka, Takahiro Hara:
Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach. SIGMOD Conference 2021: 36-48 - [c32]Daichi Amagata, Takahiro Hara:
Fast Density-Peaks Clustering: Multicore-based Parallelization Approach. SIGMOD Conference 2021: 49-61 - [i3]Shohei Tsuruoka, Daichi Amagata, Shunya Nishio, Takahiro Hara:
Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub. CoRR abs/2101.12417 (2021) - [i2]Daichi Amagata, Takahiro Hara:
Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item? CoRR abs/2110.07131 (2021) - [i1]Daichi Amagata, Makoto Onizuka, Takahiro Hara:
Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach. CoRR abs/2110.08959 (2021) - 2020
- [j12]Boqi Gao, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Detecting Reinforcement Learning-Based Grey Hole Attack in Mobile Wireless Sensor Networks. IEICE Trans. Commun. 103-B(5): 504-516 (2020) - [j11]Boqi Gao, Daichi Amagata, Takuya Maekawa, Takahiro Hara:
Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks. J. Inf. Process. 28: 689-698 (2020) - [c31]Shohei Tsuruoka, Daichi Amagata, Shunya Nishio, Takahiro Hara:
Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub. SIGSPATIAL/GIS 2020: 111-114 - [c30]Duc Nguyen, Hao Niu, Kei Yonekawa, Mori Kurokawa, Chihiro Ono, Daichi Amagata, Takuya Maekawa, Takahiro Hara:
On the Transferability of Deep Neural Networks for Recommender System. IAL@PKDD/ECML 2020: 22-37
2010 – 2019
- 2019
- [c29]Kei Yonekawa, Hao Niu, Mori Kurokawa, Arei Kobayashi, Daichi Amagata, Takuya Maekawa, Takahiro Hara:
Advertiser-Assisted Behavioral Ad-Targeting via Denoised Distribution Induction. IEEE BigData 2019: 5611-5619 - [c28]Shinya Kato, Daichi Amagata, Shunya Nishio, Takahiro Hara:
Discord Monitoring for Streaming Time-Series. DEXA (1) 2019: 79-94 - [c27]Daichi Amagata, Takahiro Hara:
Correlation Set Discovery on Time-Series Data. DEXA (2) 2019: 275-290 - [c26]Naoya Yoshimura, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Upsampling Inertial Sensor Data from Wearable Smart Devices using Neural Networks. ICDCS 2019: 1983-1993 - [c25]Daichi Amagata, Takahiro Hara, Chuan Xiao:
Dynamic Set kNN Self-Join. ICDE 2019: 818-829 - [c24]Daichi Amagata, Takahiro Hara:
Identifying the Most Interactive Object in Spatial Databases. ICDE 2019: 1286-1297 - [c23]Kei Yonekawa, Hao Niu, Mori Kurokawa, Arei Kobayashi, Daichi Amagata, Takuya Maekawa, Takahiro Hara:
A Heterogeneous Domain Adversarial Neural Network for Trans-Domain Behavioral Targeting. PAKDD (Workshops) 2019: 274-285 - [c22]Cheng Chen, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Preliminary Investigation of Predicting Next-Use Mobile Apps Using App Semantic Representations. PerCom Workshops 2019: 391-394 - [c21]Toshimitsu Kamiya, Tatsuya Nakamura, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Estimating User Contexts from Mobile Application Usage Histories. PerCom Workshops 2019: 765-770 - [c20]Yuan Lyu, Daichi Amagata, Takuya Maekawa, Takahiro Hara, Hao Niu, Kei Yonekawa, Mori Kurokawa:
Behavior Matching between Different Domains based on Canonical Correlation Analysis. WWW (Companion Volume) 2019: 361-366 - [c19]Hanxin Wang, Daichi Amagata, Takuya Maekawa, Takahiro Hara, Hao Niu, Kei Yonekawa, Mori Kurokawa:
Preliminary Investigation of Alleviating User Cold-Start Problem in E-commerce with Deep Cross-Domain Recommender System. WWW (Companion Volume) 2019: 398-403 - 2018
- [j10]Shuhei Hayashida, Daichi Amagata, Takahiro Hara, Xing Xie:
Dummy Generation Based on User-Movement Estimation for Location Privacy Protection. IEEE Access 6: 22958-22969 (2018) - [j9]Thilina Dissanayake, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Detecting Door Events Using a Smartphone via Active Sound Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(4): 160:1-160:26 (2018) - [j8]Daichi Amagata, Takahiro Hara, Makoto Onizuka:
Space Filling Approach for Distributed Processing of Top-k Dominating Queries. IEEE Trans. Knowl. Data Eng. 30(6): 1150-1163 (2018) - [c18]Mori Kurokawa, Hao Niu, Kei Yonekawa, Arei Kobayashi, Daichi Amagata, Takuya Maekawa, Takahiro Hara:
Virtual Touch-Point: Trans-Domain Behavioral Targeting via Transfer Learning. IEEE BigData 2018: 4762-4767 - [c17]Shinya Kato, Daichi Amagata, Shunya Nishio, Takahiro Hara:
Monitoring Range Motif on Streaming Time-Series. DEXA (1) 2018: 251-266 - [c16]Yujia Wang, Chalermpon Kongjit, Takahiro Hara, Daichi Amagata:
Developing a Competency-based System to Enhance Knowledge Management Program. iCAST 2018: 185-190 - [c15]Boqi Gao, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Environment-Adaptive Malicious Node Detection in MANETs with Ensemble Learning. ICDCS 2018: 556-566 - [c14]Daichi Amagata, Takahiro Hara:
Mining Top-k Co-Occurrence Patterns across Multiple Streams (Extended Abstract). ICDE 2018: 1747-1748 - [c13]Naoya Yoshimura, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Preliminary Investigation of Fine-Grained Gesture Recognition With Signal Super-Resolution. PerCom Workshops 2018: 484-487 - [c12]Thilina Dissanayake, Takuya Maekawa, Daichi Amagata, Takahiro Hara:
Preliminary Investigation of Detecting Events of Indoor Objects with Smartphone Active Sound Sensing. PerCom Workshops 2018: 500-503 - 2017
- [j7]Daichi Amagata, Takahiro Hara, Yuya Sasaki, Shojiro Nishio:
Efficient cluster-based top-k query routing with data replication in MANETs. Soft Comput. 21(15): 4161-4178 (2017) - [j6]Daichi Amagata, Takahiro Hara:
Mining Top-k Co-Occurrence Patterns across Multiple Streams. IEEE Trans. Knowl. Data Eng. 29(10): 2249-2262 (2017) - [j5]Daichi Amagata, Takahiro Hara:
A General Framework for MaxRS and MaxCRS Monitoring in Spatial Data Streams. ACM Trans. Spatial Algorithms Syst. 3(1): 1:1-1:34 (2017) - [c11]Yuki Nakayama, Daichi Amagata, Takahiro Hara:
Probabilistic MaxRS Queries on Uncertain Data. DEXA (1) 2017: 111-119 - [c10]Shunya Nishio, Daichi Amagata, Takahiro Hara:
Geo-Social Keyword Top-k Data Monitoring over Sliding Window. DEXA (1) 2017: 409-424 - [c9]Naoya Taguchi, Daichi Amagata, Takahiro Hara:
Geo-Social Keyword Skyline Queries. DEXA (1) 2017: 425-435 - 2016
- [j4]Daichi Amagata, Yuya Sasaki, Takahiro Hara, Shojiro Nishio:
Probabilistic nearest neighbor query processing on distributed uncertain data. Distributed Parallel Databases 34(2): 259-287 (2016) - [j3]Daichi Amagata, Takahiro Hara, Shojiro Nishio:
Sliding window top-k dominating query processing over distributed data streams. Distributed Parallel Databases 34(4): 535-566 (2016) - [j2]Daichi Amagata, Yuya Sasaki, Takahiro Hara, Shojiro Nishio:
Efficient processing of top-k dominating queries in distributed environments. World Wide Web 19(4): 545-577 (2016) - [c8]Daichi Amagata, Takahiro Hara:
Diversified set monitoring over distributed data streams. DEBS 2016: 1-12 - [c7]Yuki Nakayama, Daichi Amagata, Takahiro Hara:
An Efficient Method for Identifying MaxRS Location in Mobile Ad Hoc Networks. DEXA (1) 2016: 37-51 - [c6]Daichi Amagata, Takahiro Hara:
Monitoring MaxRS in Spatial Data Streams. EDBT 2016: 317-328 - 2015
- [j1]Daichi Amagata, Yuya Sasaki, Takahiro Hara, Shojiro Nishio:
Efficient Multidimensional Top-k Query Processing in Wireless Multihop Networks. Mob. Inf. Syst. 2015: 657431:1-657431:20 (2015) - [c5]Yuki Nakayama, Daichi Amagata, Takahiro Hara, Shojiro Nishio:
Range-based Continuous Threshold Query Processing in Mobile Ad Hoc Networks. MoMM 2015: 169-178 - [c4]Daichi Amagata, Takahiro Hara, Shojiro Nishio:
Distributed top-k query processing on multi-dimensional data with keywords. SSDBM 2015: 10:1-10:12 - 2014
- [c3]Daichi Amagata, Yuya Sasaki, Takahiro Hara, Shojiro Nishio:
CTR: An Efficient Cluster-based Top-k Query Routing in MANETs. MoMM 2014: 225-234 - 2013
- [c2]Daichi Amagata, Yuya Sasaki, Takahiro Hara, Shojiro Nishio:
A Routing Method for Top-k Query Processing in Mobile Ad Hoc Networks. AINA 2013: 161-168 - [c1]Daichi Amagata, Yuya Sasaki, Takahiro Hara, Shojiro Nishio:
A Robust Routing Method for Top-k Queries in Mobile Ad Hoc Networks. MDM (1) 2013: 251-256
Coauthor Index
[j21] [c59] [c58] [c57] [c55] [i10] [j20] [j19] [c52] [c51] [c50] [c49] [c48] [c47] [c46] [i7] [j18] [j17] [j16] [j15] [j14] [c44] [c43] [c42] [c41] [c40] [c39] [i6] [i5] [i4] [j13] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [i3] [i2] [i1] [j12] [j11] [c31] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [j10] [j9] [j8] [c18] [c17] [c16] [c15] [c14] [c13] [c12] [j7] [j6] [j5] [c11] [c10] [c9] [j4] [j3] [j2] [c8] [c7] [c6] [j1] [c5] [c4] [c3] [c2] [c1]
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