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2020 – today
- 2024
[j60]Naoki Marumo
, Akiko Takeda:
Parameter-Free Accelerated Gradient Descent for Nonconvex Minimization. SIAM J. Optim. 34(2): 2093-2120 (2024)- [j59]Mitsuaki Obara, Kazuhiro Sato, Hiroki Sakamoto, Takayuki Okuno, Akiko Takeda:
Stable Linear System Identification With Prior Knowledge by Riemannian Sequential Quadratic Optimization. IEEE Trans. Autom. Control. 69(3): 2060-2066 (2024) - [i15]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda:
A Framework for Bilevel Optimization on Riemannian Manifolds. CoRR abs/2402.03883 (2024) - [i14]Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Jawanpuria, Bamdev Mishra:
SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining. CoRR abs/2406.02214 (2024) - 2023
- [j58]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) - [j57]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) - [j56]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): 555-598 (2023) - [j55]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) - [c24]Daniel Andrade, Akiko Takeda:
Robust Gaussian process regression with the trimmed marginal likelihood. UAI 2023: 67-76 - 2022
- [j54]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) - [j53]Michael R. Metel, Akiko Takeda:
Perturbed Iterate SGD for Lipschitz Continuous Loss Functions. J. Optim. Theory Appl. 195(2): 504-547 (2022) - [j52]Mitsuaki Obara, Takayuki Okuno, Akiko Takeda:
Sequential Quadratic Optimization for Nonlinear Optimization Problems on Riemannian Manifolds. SIAM J. Optim. 32(2): 822-853 (2022) - [j51]Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda:
Convexification with Bounded Gap for Randomly Projected Quadratic Optimization. SIAM J. Optim. 32(2): 874-899 (2022) - [c23]Hidenori Iwakiri, Yuhang Wang, Shinji Ito, Akiko Takeda:
Single Loop Gaussian Homotopy Method for Non-convex Optimization. NeurIPS 2022 - [i13]Issey Sukeda, Atsushi Miyauchi, Akiko Takeda:
A Study on Modularity Density Maximization: Column Generation Acceleration and Computational Complexity Analysis. CoRR abs/2206.10901 (2022) - 2021
- [j50]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) - [j49]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) - [j48]Michael R. Metel, Akiko Takeda:
Primal-dual subgradient method for constrained convex optimization problems. Optim. Lett. 15(4): 1491-1504 (2021) - [c22]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 - [c21]Ryo Sato, Mirai Tanaka, Akiko Takeda:
A Gradient Method for Multilevel Optimization. NeurIPS 2021: 7522-7533 - [i12]Yuto Mori, Atsushi Nitanda, Akiko Takeda:
BODAME: Bilevel Optimization for Defense Against Model Extraction. CoRR abs/2103.06797 (2021) - [i11]Ryo Sato, Mirai Tanaka, Akiko Takeda:
A Gradient Method for Multilevel Optimization. CoRR abs/2105.13954 (2021) - [i10]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) - 2020
- [j47]Kazuhiro Sato, Akiko Takeda:
Construction Methods of the Nearest Positive System. IEEE Control. Syst. Lett. 4(1): 97-102 (2020) - [j46]Kazuhiro Sato, Akiko Takeda:
Controllability Maximization of Large-Scale Systems Using Projected Gradient Method. IEEE Control. Syst. Lett. 4(4): 821-826 (2020) - [j45]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) - [j44]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: 285-292 (2020) - [j43]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) - [j42]Bruno F. Lourenço, Akiko Takeda:
Generalized Subdifferentials of Spectral Functions over Euclidean Jordan Algebras. SIAM J. Optim. 30(4): 3387-3414 (2020) - [j41]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) - [j40]Ivan Markovsky, Tianxiang Liu, Akiko Takeda:
Data-Driven Structured Noise Filtering via Common Dynamics Estimation. IEEE Trans. Signal Process. 68: 3064-3073 (2020) - [c20]Hikaru Ogura, Akiko Takeda:
Convex Fairness Constrained Model Using Causal Effect Estimators. WWW (Companion Volume) 2020: 723-732 - [i9]Hikaru Ogura, Akiko Takeda:
Convex Fairness Constrained Model Using Causal Effect Estimators. CoRR abs/2002.06501 (2020) - [i8]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)
2010 – 2019
- 2019
- [j39]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): 69-100 (2019) - [j38]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) - [j37]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 Complementarity Constraints. ACM Trans. Math. Softw. 45(3): 34:1-34:26 (2019) - [c19]Ivan Markovsky, Tianxiang Liu, Akiko Takeda:
Subspace methods for multi-channel sum-of-exponentials common dynamics estimation. CDC 2019: 2672-2675 - [c18]Michael R. Metel, Akiko Takeda:
Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization. ICML 2019: 4537-4545 - 2018
- [j36]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) - [j35]Shinji Yamada, Akiko Takeda:
Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization. J. Glob. Optim. 71(2): 313-339 (2018) - [j34]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 (2018) - [j33]Jun-ya Gotoh, Akiko Takeda, Katsuya Tono:
DC formulations and algorithms for sparse optimization problems. Math. Program. 169(1): 141-176 (2018) - [c17]Atsushi Miyauchi, Akiko Takeda:
Robust Densest Subgraph Discovery. ICDM 2018: 1188-1193 - [c16]Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao:
Nonconvex Optimization for Regression with Fairness Constraints. ICML 2018: 2742-2751 - [i7]Atsushi Miyauchi, Akiko Takeda:
Robust Densest Subgraph Discovery. CoRR abs/1809.04802 (2018) - [i6]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) - 2017
- [j32]Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda:
Breakdown Point of Robust Support Vector Machines. Entropy 19(2): 83 (2017) - [j31]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) - [j30]Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori:
DC Algorithm for Extended Robust Support Vector Machine. Neural Comput. 29(5): 1406-1438 (2017) - [j29]Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda:
Robustness of learning algorithms using hinge loss with outlier indicators. Neural Networks 94: 173-191 (2017) - [j28]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) - [j27]Shinsaku Sakaue, Akiko Takeda, Sunyoung Kim, Naoki Ito:
Exact Semidefinite Programming Relaxations with Truncated Moment Matrix for Binary Polynomial Optimization Problems. SIAM J. Optim. 27(1): 565-582 (2017) - [c15]Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu:
Trimmed Density Ratio Estimation. NIPS 2017: 4518-4528 - [c14]Junpei Komiyama, Junya Honda, Akiko Takeda:
Position-based Multiple-play Bandit Problem with Unknown Position Bias. NIPS 2017: 4998-5008 - [c13]Kosuke Nishida, Akiko Takeda, Satoru Iwata, Mariko Kiho, Isao Nakayama:
Household energy consumption prediction by feature selection of lifestyle data. SmartGridComm 2017: 235-240 - [i5]Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda:
Boosting the kernelized shapelets: Theory and algorithms for local features. CoRR abs/1709.01300 (2017) - [i4]Matthew Norton, Akiko Takeda, Alexander Mafusalov:
Optimistic Robust Optimization With Applications To Machine Learning. CoRR abs/1711.07511 (2017) - 2016
- [j26]Shinsaku Sakaue, Yuji Nakatsukasa, Akiko Takeda, Satoru Iwata:
Solving Generalized CDT Problems via Two-Parameter Eigenvalues. SIAM J. Optim. 26(3): 1669-1694 (2016) - 2015
- [j25]Yawwani Gunawardana, Shuhei Fujiwara, Akiko Takeda, Jeongmin Woo, Christopher H. Woelk, Mahesan Niranjan:
Outlier detection at the transcriptome-proteome interface. Bioinform. 31(15): 2530-2536 (2015) - [j24]Dimitris Bertsimas, Akiko Takeda:
Optimizing over coherent risk measures and non-convexities: a robust mixed integer optimization approach. Comput. Optim. Appl. 62(3): 613-639 (2015) - [j23]Álvaro Barbero Jiménez, Akiko Takeda, Jorge López Lázaro:
Geometric intuition and algorithms for Ev-SVM. J. Mach. Learn. Res. 16: 323-369 (2015) - [j22]Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda:
Computing the Signed Distance Between Overlapping Ellipsoids. SIAM J. Optim. 25(4): 2359-2384 (2015) - [j21]Yutaro Yamaguchi, Anna Ogawa, Akiko Takeda, Satoru Iwata:
Cyber Security Analysis of Power Networks by Hypergraph Cut Algorithms. IEEE Trans. Smart Grid 6(5): 2189-2199 (2015) - [c12]Shuichi Katsumata, Akiko Takeda:
Robust Cost Sensitive Support Vector Machine. AISTATS 2015 - 2014
- [j20]Takafumi Kanamori, Akiko Takeda:
Numerical study of learning algorithms on Stiefel manifold. Comput. Manag. Sci. 11(4): 319-340 (2014) - [j19]Jun-ya Gotoh, Akiko Takeda, Rei Yamamoto:
Interaction between financial risk measures and machine learning methods. Comput. Manag. Sci. 11(4): 365-402 (2014) - [j18]Akiko Takeda, Shuhei Fujiwara, Takafumi Kanamori:
Extended Robust Support Vector Machine Based on Financial Risk Minimization. Neural Comput. 26(11): 2541-2569 (2014) - [j17]Akiko Takeda, Takafumi Kanamori:
Using financial risk measures for analyzing generalization performance of machine learning models. Neural Networks 57: 29-38 (2014) - [c11]Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda:
Global Optimization Methods for Extended Fisher Discriminant Analysis. AISTATS 2014: 411-419 - [c10]Abdullah Alrajeh, Akiko Takeda, Mahesan Niranjan:
Memory-efficient large-scale linear support vector machine. ICMV 2014: 944527 - [c9]Masashi Kitamura, Akiko Takeda, Satoru Iwata:
Exact SVM training by Wolfe's minimum norm point algorithm. MLSP 2014: 1-6 - [c8]Yutaro Yamaguchi, Anna Ogawa, Akiko Takeda, Satoru Iwata:
Cyber security analysis of power networks by hypergraph cut algorithms. SmartGridComm 2014: 824-829 - [i3]Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda:
Breakdown Point of Robust Support Vector Machine. CoRR abs/1409.0934 (2014) - 2013
- [j16]Akiko Takeda, Mahesan Niranjan, Jun-ya Gotoh, Yoshinobu Kawahara:
Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios. Comput. Manag. Sci. 10(1): 21-49 (2013) - [j15]Takafumi Kanamori, Akiko Takeda, Taiji Suzuki:
Conjugate relation between loss functions and uncertainty sets in classification problems. J. Mach. Learn. Res. 14(1): 1461-1504 (2013) - [j14]Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori:
A Unified Classification Model Based on Robust Optimization. Neural Comput. 25(3): 759-804 (2013) - [c7]Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi:
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. NIPS 2013: 1439-1447 - [c6]Naoki Ito, Akiko Takeda, Toru Namerikawa:
Convex hull pricing for demand response in electricity markets. SmartGridComm 2013: 151-156 - 2012
- [j13]Jun-ya Gotoh, Akiko Takeda:
Minimizing loss probability bounds for portfolio selection. Eur. J. Oper. Res. 217(2): 371-380 (2012) - [j12]Takafumi Kanamori, Akiko Takeda:
Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty. J. Optim. Theory Appl. 152(1): 171-197 (2012) - [c5]Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori:
A Unified Robust Classification Model. ICML 2012 - [c4]Takafumi Kanamori, Akiko Takeda:
Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning. ICONIP (1) 2012: 109-116 - [c3]Takafumi Kanamori, Akiko Takeda, Taiji Suzuki:
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. COLT 2012: 29.1-29.23 - [i2]Takafumi Kanamori, Akiko Takeda, Taiji Suzuki:
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. CoRR abs/1204.6583 (2012) - [i1]Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori:
A Unified Robust Classification Model. CoRR abs/1206.4599 (2012) - 2011
- [j11]Jun-ya Gotoh, Akiko Takeda:
On the role of norm constraints in portfolio selection. Comput. Manag. Sci. 8(4): 323 (2011) - 2010
- [j10]Akiko Takeda, Shunsuke Taguchi, Tsutomu Tanaka:
A relaxation algorithm with a probabilistic guarantee for robust deviation optimization. Comput. Optim. Appl. 47(1): 1-31 (2010)
2000 – 2009
- 2009
- [j9]Akiko Takeda, Takafumi Kanamori:
A robust approach based on conditional value-at-risk measure to statistical learning problems. Eur. J. Oper. Res. 198(1): 287-296 (2009) - [j8]Akiko Takeda:
Generalization performance of nu-support vector classifier based on conditional value-at-risk minimization. Neurocomputing 72(10-12): 2351-2358 (2009) - [j7]Akiko Takeda, Masashi Sugiyama:
On Generalization Performance and Non-Convex Optimization of Extended nu-Support Vector Machine. New Gener. Comput. 27(3): 259-279 (2009) - 2008
- [j6]Jun-ya Gotoh, Akiko Takeda:
Conditional minimum volume ellipsoid with application to multiclass discrimination. Comput. Optim. Appl. 41(1): 27-51 (2008) - [c2]Akiko Takeda, Masashi Sugiyama:
nu-support vector machine as conditional value-at-risk minimization. ICML 2008: 1056-1063 - 2007
- [j5]Tomohiko Mizutani, Akiko Takeda, Masakazu Kojima:
Dynamic Enumeration of All Mixed Cells. Discret. Comput. Geom. 37(3): 351-367 (2007) - 2004
- [j4]Takayuki Gunji, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Katsuki Fujisawa, Tomohiko Mizutani:
PHoM - a Polyhedral Homotopy Continuation Method for Polynomial Systems. Computing 73(1): 57-77 (2004) - [c1]Katsuki Fujisawa, Masakazu Kojima, Akiko Takeda, Makoto Yamashita:
High Performance Grid and Cluster Computing for Some Optimization Problems. SAINT Workshops 2004: 612-615 - 2002
- [j3]Akiko Takeda, Katsuki Fujisawa, Yusuke Fukaya, Masakazu Kojima:
Parallel Implementation of Successive Convex Relaxation Methods for Quadratic Optimization Problems. J. Glob. Optim. 24(2): 237-260 (2002) - 2001
- [j2]Akiko Takeda, Hisakazu Nishino:
On measuring the inefficiency with the inner-product norm in data envelopment analysis. Eur. J. Oper. Res. 133(2): 377-393 (2001) - [j1]Masakazu Kojima, Akiko Takeda:
Complexity Analysis of Successive Convex Relaxation Methods for Nonconvex Sets. Math. Oper. Res. 26(3): 519-542 (2001)
Coauthor Index
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