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Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem.
Satoru Adachi, Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda: Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem. SIAM J. Optim. 27(1): 269-291 (2017)
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Robustness of learning algorithms using hinge loss with outlier indicators.
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda: Robustness of learning algorithms using hinge loss with outlier indicators. Neural Networks 94: 173-191 (2017)
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DC Algorithm for Extended Robust Support Vector Machine.
Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori: DC Algorithm for Extended Robust Support Vector Machine. Neural Comput. 29(5): 1406-1438 (2017)
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A Unified Formulation and Fast Accelerated Proximal Gradient Method for...
Naoki Ito, Akiko Takeda, Kim-Chuan Toh: A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification. J. Mach. Learn. Res. 18: 16:1-16:49 (2017)
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Breakdown Point of Robust Support Vector Machines.
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda: Breakdown Point of Robust Support Vector Machines. Entropy 19(2): 83 (2017)
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Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based...
Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda: Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers. CoRR abs/1811.08084 (2018)
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Robust Densest Subgraph Discovery.
Atsushi Miyauchi, Akiko Takeda: Robust Densest Subgraph Discovery. CoRR abs/1809.04802 (2018)
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Nonconvex Optimization for Regression with Fairness Constraints.
Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao: Nonconvex Optimization for Regression with Fairness Constraints. ICML 2018: 2742-2751
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Robust Densest Subgraph Discovery.
Atsushi Miyauchi, Akiko Takeda: Robust Densest Subgraph Discovery. ICDM 2018: 1188-1193
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DC formulations and algorithms for sparse optimization problems.
Jun-ya Gotoh, Akiko Takeda, Katsuya Tono: DC formulations and algorithms for sparse optimization problems. Math. Program. 169(1): 141-176 (2018)
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Equivalences and differences in conic relaxations of combinatorial quadratic...
Naoki Ito, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Kim-Chuan Toh: Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems. J. Glob. Optim. 72(4): 619-653...
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Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization.
Shinji Yamada, Akiko Takeda: Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization. J. Glob. Optim. 71(2): 313-339 (2018)
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Improving cash logistics in bank branches by coupling machine learning and...
Jorge López Lázaro, Álvaro Barbero Jiménez, Akiko Takeda: Improving cash logistics in bank branches by coupling machine learning and robust optimization. Expert Syst. Appl. 92: 236-255 (2018)
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Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized...
Michael R. Metel, Akiko Takeda: Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization. ICML 2019: 4537-4545
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Subspace methods for multi-channel sum-of-exponentials common dynamics...
Ivan Markovsky, Tianxiang Liu, Akiko Takeda: Subspace methods for multi-channel sum-of-exponentials common dynamics estimation. CDC 2019: 2672-2675
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Algorithm 996: BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial...
Naoki Ito, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Kim-Chuan Toh: Algorithm 996: BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems With Binary, Box, and...
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A successive difference-of-convex approximation method for a class of...
Tianxiang Liu, Ting Kei Pong, Akiko Takeda: A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems. Math. Program. 176(1-2): 339-367 (2019)
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A refined convergence analysis of \(\hbox {pDCA}_{e}\) with applications to...
Tianxiang Liu, Ting Kei Pong, Akiko Takeda: A refined convergence analysis of \(\hbox {pDCA}_{e}\) with applications to simultaneous sparse recovery and outlier detection. Comput. Optim. Appl. 73(1):...
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Theory and Algorithms for Shapelet-based Multiple-Instance Learning.
Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda: Theory and Algorithms for Shapelet-based Multiple-Instance Learning. CoRR abs/2006.01130 (2020)
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Convex Fairness Constrained Model Using Causal Effect Estimators.
Hikaru Ogura, Akiko Takeda: Convex Fairness Constrained Model Using Causal Effect Estimators. CoRR abs/2002.06501 (2020)
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Convex Fairness Constrained Model Using Causal Effect Estimators.
Hikaru Ogura, Akiko Takeda: Convex Fairness Constrained Model Using Causal Effect Estimators. WWW (Companion Volume) 2020: 723-732
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Data-Driven Structured Noise Filtering via Common Dynamics Estimation.
Ivan Markovsky, Tianxiang Liu, Akiko Takeda: Data-Driven Structured Noise Filtering via Common Dynamics Estimation. IEEE Trans. Signal Process. 68: 3064-3073 (2020)
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A Hybrid Penalty Method for a Class of Optimization Problems with Multiple...
Tianxiang Liu, Ivan Markovsky, Ting Kei Pong, Akiko Takeda: A Hybrid Penalty Method for a Class of Optimization Problems with Multiple Rank Constraints. SIAM J. Matrix Anal. Appl. 41(3): 1260-1283 (2020)
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Generalized Subdifferentials of Spectral Functions over Euclidean Jordan...
Bruno F. Lourenço, Akiko Takeda: Generalized Subdifferentials of Spectral Functions over Euclidean Jordan Algebras. SIAM J. Optim. 30(4): 3387-3414 (2020)
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Robust Bayesian model selection for variable clustering with the Gaussian...
Daniel Andrade, Akiko Takeda, Kenji Fukumizu: Robust Bayesian model selection for variable clustering with the Gaussian graphical model. Stat. Comput. 30(2): 351-376 (2020)
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Estimation of Gaussian mixture models via tensor moments with application to...
Donya Rahmani, Mahesan Niranjan, Damien Fay, Akiko Takeda, Jacek Brodzki: Estimation of Gaussian mixture models via tensor moments with application to online learning. Pattern Recognit. Lett. 131:...
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Theory and Algorithms for Shapelet-Based Multiple-Instance Learning.
Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda: Theory and Algorithms for Shapelet-Based Multiple-Instance Learning. Neural Comput. 32(8): 1580-1613 (2020)
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Controllability Maximization of Large-Scale Systems Using Projected Gradient...
Kazuhiro Sato, Akiko Takeda: Controllability Maximization of Large-Scale Systems Using Projected Gradient Method. IEEE Control. Syst. Lett. 4(4): 821-826 (2020)
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Construction Methods of the Nearest Positive System.
Kazuhiro Sato, Akiko Takeda: Construction Methods of the Nearest Positive System. IEEE Control. Syst. Lett. 4(1): 97-102 (2020)
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A Projected Gradient Method for Opinion Optimization with Limited Changes of...
Naoki Marumo, Atsushi Miyauchi, Akiko Takeda, Akira Tanaka: A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion. CoRR abs/2108.09865 (2021)
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A Gradient Method for Multilevel Optimization.
Ryo Sato, Mirai Tanaka, Akiko Takeda: A Gradient Method for Multilevel Optimization. CoRR abs/2105.13954 (2021)
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BODAME: Bilevel Optimization for Defense Against Model Extraction.
Yuto Mori, Atsushi Nitanda, Akiko Takeda: BODAME: Bilevel Optimization for Defense Against Model Extraction. CoRR abs/2103.06797 (2021)
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A Gradient Method for Multilevel Optimization.
Ryo Sato, Mirai Tanaka, Akiko Takeda: A Gradient Method for Multilevel Optimization. NeurIPS 2021: 7522-7533
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A Projected Gradient Method for Opinion Optimization with Limited Changes of...
Naoki Marumo, Atsushi Miyauchi, Akiko Takeda, Akira Tanaka: A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion. CIKM 2021: 1274-1283
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Primal-dual subgradient method for constrained convex optimization problems.
Michael R. Metel, Akiko Takeda: Primal-dual subgradient method for constrained convex optimization problems. Optim. Lett. 15(4): 1491-1504 (2021)
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On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization.
Takayuki Okuno, Akiko Takeda, Akihiro Kawana, Motokazu Watanabe: On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization. J. Mach. Learn. Res. 22: 245:1-245:47 (2021)
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Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse...
Michael R. Metel, Akiko Takeda: Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization. J. Mach. Learn. Res. 22: 115:1-115:36 (2021)
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A Study on Modularity Density Maximization: Column Generation Acceleration...
Issey Sukeda, Atsushi Miyauchi, Akiko Takeda: A Study on Modularity Density Maximization: Column Generation Acceleration and Computational Complexity Analysis. CoRR abs/2206.10901 (2022)
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Single Loop Gaussian Homotopy Method for Non-convex Optimization.
Hidenori Iwakiri, Yuhang Wang, Shinji Ito, Akiko Takeda: Single Loop Gaussian Homotopy Method for Non-convex Optimization. NeurIPS 2022
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Convexification with Bounded Gap for Randomly Projected Quadratic Optimization.
Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda: Convexification with Bounded Gap for Randomly Projected Quadratic Optimization. SIAM J. Optim. 32(2): 874-899 (2022)
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Sequential Quadratic Optimization for Nonlinear Optimization Problems on...
Mitsuaki Obara, Takayuki Okuno, Akiko Takeda: Sequential Quadratic Optimization for Nonlinear Optimization Problems on Riemannian Manifolds. SIAM J. Optim. 32(2): 822-853 (2022)
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Perturbed Iterate SGD for Lipschitz Continuous Loss Functions.
Michael R. Metel, Akiko Takeda: Perturbed Iterate SGD for Lipschitz Continuous Loss Functions. J. Optim. Theory Appl. 195(2): 504-547 (2022)
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An inexact successive quadratic approximation method for a class of...
Tianxiang Liu, Akiko Takeda: An inexact successive quadratic approximation method for a class of difference-of-convex optimization problems. Comput. Optim. Appl. 82(1): 141-173 (2022)
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Robust Gaussian process regression with the trimmed marginal likelihood.
Daniel Andrade, Akiko Takeda: Robust Gaussian process regression with the trimmed marginal likelihood. UAI 2023: 67-76
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A study on modularity density maximization: Column generation acceleration...
Issey Sukeda, Atsushi Miyauchi, Akiko Takeda: A study on modularity density maximization: Column generation acceleration and computational complexity analysis. Eur. J. Oper. Res. 309(2): 516-528 (2023)
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Complexity analysis of interior-point methods for second-order stationary...
Shun Arahata, Takayuki Okuno, Akiko Takeda: Complexity analysis of interior-point methods for second-order stationary points of nonlinear semidefinite optimization problems. Comput. Optim. Appl. 86(2):...
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Doubly majorized algorithm for sparsity-inducing optimization problems with...
Tianxiang Liu, Ting Kei Pong, Akiko Takeda: Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints. Comput. Optim. Appl. 86(2): 521-553 (2023)
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Majorization-minimization-based Levenberg-Marquardt method for constrained...
Naoki Marumo, Takayuki Okuno, Akiko Takeda: Majorization-minimization-based Levenberg-Marquardt method for constrained nonlinear least squares. Comput. Optim. Appl. 84(3): 833-874 (2023)
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A Framework for Bilevel Optimization on Riemannian Manifolds.
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda: A Framework for Bilevel Optimization on Riemannian Manifolds. CoRR abs/2402.03883 (2024)
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Stable Linear System Identification With Prior Knowledge by Riemannian...
Mitsuaki Obara, Kazuhiro Sato, Hiroki Sakamoto, Takayuki Okuno, Akiko Takeda: Stable Linear System Identification With Prior Knowledge by Riemannian Sequential Quadratic Optimization. IEEE Trans....
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