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2020 – today
- 2024
[c18]Tianchen Zhou, Hairi, Haibo Yang, Jia Liu, Tian Tong, Fan Yang, Michinari Momma, Yan Gao:
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning. ICML 2024- [i7]Tianchen Zhou, Hairi, Haibo Yang, Jia Liu, Tian Tong, Fan Yang, Michinari Momma, Yan Gao:
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning. CoRR abs/2405.03082 (2024) - 2023
- [c17]Debabrata Mahapatra
, Chaosheng Dong, Michinari Momma:
Querywise Fair Learning to Rank through Multi-Objective Optimization. KDD 2023: 1653-1664 - [c16]Debabrata Mahapatra, Chaosheng Dong, Yetian Chen, Michinari Momma:
Multi-Label Learning to Rank through Multi-Objective Optimization. KDD 2023: 4605-4616 - [c15]Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma:
Federated Multi-Objective Learning. NeurIPS 2023 - [c14]Haoming Chen, Yetian Chen, Jingjing Meng, Yang Jiao, Yikai Ni, Yan Gao, Michinari Momma, Yi Sun:
Improving Product Search with Season-Aware Query-Product Semantic Similarity. WWW (Companion Volume) 2023: 864-868 - [i6]Minghe Zhang, Chaosheng Dong, Jinmiao Fu, Tianchen Zhou, Jia Liang, Jia Liu, Bo Liu, Michinari Momma, Bryan Wang, Yan Gao, Yi Sun:
AdaSelection: Accelerating Deep Learning Training through Data Subsampling. CoRR abs/2306.10728 (2023) - [i5]Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma:
Federated Multi-Objective Learning. CoRR abs/2310.09866 (2023) - 2022
- [c13]Michinari Momma, Chaosheng Dong, Jia Liu:
A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity. ICML 2022: 15895-15907 - [c12]Minghong Fang, Jia Liu, Michinari Momma, Yi Sun:
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data. SACMAT 2022: 173-184 - [c11]Tong Jian, Fan Yang, Zhen Zuo, Wenbo Wang, Michinari Momma, Tong Zhao, Chaosheng Dong, Yan Gao, Yi Sun:
Multi-task GNN for Substitute Identification. WWW (Companion Volume) 2022: 228-231 - [i4]Debabrata Mahapatra, Chaosheng Dong, Yetian Chen, Deqiang Meng, Michinari Momma:
Multi-Label Learning to Rank through Multi-Objective Optimization. CoRR abs/2207.03060 (2022) - [i3]Minghong Fang, Jia Liu, Michinari Momma, Yi Sun:
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data. CoRR abs/2212.06750 (2022) - 2020
- [c10]Michinari Momma, Alireza Bagheri Garakani, Nanxun Ma, Yi Sun:
Multi-objective Ranking via Constrained Optimization. WWW (Companion Volume) 2020: 111-112 - [i2]Michinari Momma, Alireza Bagheri Garakani, Nanxun Ma, Yi Sun:
Multi-objective Ranking via Constrained Optimization. CoRR abs/2002.05753 (2020)
2010 – 2019
- 2019
- [c9]Michinari Momma, Alireza Bagheri Garakani, Yi Sun:
Multi-objective Relevance Ranking. eCOM@SIGIR 2019 - 2012
- [i1]Michinari Momma, Yun Chi, Yuanqing Lin, Shenghuo Zhu, Tianbao Yang:
Influence Analysis in the Blogosphere. CoRR abs/1212.5863 (2012) - 2010
- [c8]Michinari Momma, Kohei Hatano, Hiroki Nakayama:
Ellipsoidal Support Vector Machines. ACML 2010: 31-46
2000 – 2009
- 2009
- [c7]Ryohei Fujimaki, Satoshi Morinaga, Michinari Momma, Kenji Aoki, Takayuki Nakata:
Linear Time Model Selection for Mixture of Heterogeneous Components. ACML 2009: 82-97 - [c6]Michinari Momma, Satoshi Morinaga, Daisuke Komura:
Promoting Total Efficiency in Text Clustering via Iterative and Interactive Metric Learning. ICDM 2009: 878-883 - 2006
- [p1]Michinari Momma, Kristin P. Bennett:
Constructing Orthogonal Latent Features for Arbitrary Loss. Feature Extraction 2006: 551-583 - 2005
- [c5]Michinari Momma:
Efficient computations via scalable sparse kernel partial least squares and boosted latent features. KDD 2005: 654-659 - 2003
- [j1]Joel Carleton, Daragh Hartnett, Joseph Milana, Michinari Momma, Joseph Sirosh, Gabriela Surpi:
Model Builder for Predictive Analytics & Fair Isaac's approach to KDD Cup 2003. SIGKDD Explor. 5(2): 163-164 (2003) - [c4]Tijl De Bie, Michinari Momma, Nello Cristianini:
Efficiently Learning the Metric with Side-Information. ALT 2003: 175-189 - [c3]Michinari Momma, Kristin P. Bennett:
Sparse Kernel Partial Least Squares Regression. COLT 2003: 216-230 - 2002
- [c2]Kristin P. Bennett, Michinari Momma, Mark J. Embrechts:
MARK: a boosting algorithm for heterogeneous kernel models. KDD 2002: 24-31 - [c1]Michinari Momma, Kristin P. Bennett:
A Pattern Search Method for Model Selection of Support Vector Regression. SDM 2002: 261-274
Coauthor Index
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last updated on 2024-09-04 00:30 CEST by the dblp team
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