Akiko Takeda

According to our database, Akiko Takeda authored at least 90 papers between 2001 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Stable Linear System Identification With Prior Knowledge by Riemannian Sequential Quadratic Optimization.

Mitsuaki Obara, Kazuhiro Sato, Hiroki Sakamoto, Takayuki Okuno, Akiko Takeda

IEEE Trans. Autom. Control., March, 2024

Parameter-Free Accelerated Gradient Descent for Nonconvex Minimization.

Naoki Marumo, Akiko Takeda

SIAM J. Optim., 2024

SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining.

Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Jawanpuria, Bamdev Mishra

CoRR, 2024

A Framework for Bilevel Optimization on Riemannian Manifolds.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda

CoRR, 2024

2023

Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints.

Tianxiang Liu, Ting Kei Pong, Akiko Takeda

Comput. Optim. Appl., November, 2023

Complexity analysis of interior-point methods for second-order stationary points of nonlinear semidefinite optimization problems.

Shun Arahata, Takayuki Okuno, Akiko Takeda

Comput. Optim. Appl., November, 2023

A study on modularity density maximization: Column generation acceleration and computational complexity analysis.

Issey Sukeda, Atsushi Miyauchi, Akiko Takeda

Eur. J. Oper. Res., September, 2023

Majorization-minimization-based Levenberg-Marquardt method for constrained nonlinear least squares.

Naoki Marumo, Takayuki Okuno, Akiko Takeda

Comput. Optim. Appl., April, 2023

Robust Gaussian process regression with the trimmed marginal likelihood.

Daniel Andrade, Akiko Takeda

Proceedings of the Uncertainty in Artificial Intelligence, 2023

2022

Sequential Quadratic Optimization for Nonlinear Optimization Problems on Riemannian Manifolds.

Mitsuaki Obara, Takayuki Okuno, Akiko Takeda

SIAM J. Optim., 2022

Convexification with Bounded Gap for Randomly Projected Quadratic Optimization.

Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda

SIAM J. Optim., 2022

Perturbed Iterate SGD for Lipschitz Continuous Loss Functions.

Michael R. Metel, Akiko Takeda

J. Optim. Theory Appl., 2022

An inexact successive quadratic approximation method for a class of difference-of-convex optimization problems.

Tianxiang Liu, Akiko Takeda

Comput. Optim. Appl., 2022

Single Loop Gaussian Homotopy Method for Non-convex Optimization.

Hidenori Iwakiri, Yuhang Wang, Shinji Ito, Akiko Takeda

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Primal-dual subgradient method for constrained convex optimization problems.

Michael R. Metel, Akiko Takeda

Optim. Lett., 2021

On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization.

Takayuki Okuno, Akiko Takeda, Akihiro Kawana, Motokazu Watanabe

J. Mach. Learn. Res., 2021

Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization.

Michael R. Metel, Akiko Takeda

J. Mach. Learn. Res., 2021

BODAME: Bilevel Optimization for Defense Against Model Extraction.

Yuto Mori, Atsushi Nitanda, Akiko Takeda

CoRR, 2021

A Gradient Method for Multilevel Optimization.

Ryo Sato, Mirai Tanaka, Akiko Takeda

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion.

Naoki Marumo, Atsushi Miyauchi, Akiko Takeda, Akira Tanaka

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

Data-Driven Structured Noise Filtering via Common Dynamics Estimation.

Ivan Markovsky, Tianxiang Liu, Akiko Takeda

IEEE Trans. Signal Process., 2020

A Hybrid Penalty Method for a Class of Optimization Problems with Multiple Rank Constraints.

Tianxiang Liu, Ivan Markovsky, Ting Kei Pong, Akiko Takeda

SIAM J. Matrix Anal. Appl., 2020

Generalized Subdifferentials of Spectral Functions over Euclidean Jordan Algebras.

Bruno F. Lourenço, Akiko Takeda

SIAM J. Optim., 2020

Robust Bayesian model selection for variable clustering with the Gaussian graphical model.

Daniel Andrade, Akiko Takeda, Kenji Fukumizu

Stat. Comput., 2020

Estimation of Gaussian mixture models via tensor moments with application to online learning.

Donya Rahmani, Mahesan Niranjan, Damien Fay, Akiko Takeda, Jacek Brodzki

Pattern Recognit. Lett., 2020

Theory and Algorithms for Shapelet-Based Multiple-Instance Learning.

Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda

Neural Comput., 2020

Controllability Maximization of Large-Scale Systems Using Projected Gradient Method.

Kazuhiro Sato, Akiko Takeda

IEEE Control. Syst. Lett., 2020

Construction Methods of the Nearest Positive System.

Kazuhiro Sato, Akiko Takeda

IEEE Control. Syst. Lett., 2020

Convex Fairness Constrained Model Using Causal Effect Estimators.

Hikaru Ogura, Akiko Takeda

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

2019

Algorithm 996: BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems With Binary, Box, and Complementarity Constraints.

Naoki Ito, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Kim-Chuan Toh

ACM Trans. Math. Softw., 2019

A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems.

Tianxiang Liu, Ting Kei Pong, Akiko Takeda

Math. Program., 2019

A refined convergence analysis of \(\hbox {pDCA}_{e}\) with applications to simultaneous sparse recovery and outlier detection.

Tianxiang Liu, Ting Kei Pong, Akiko Takeda

Comput. Optim. Appl., 2019

Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization.

Michael R. Metel, Akiko Takeda

Proceedings of the 36th International Conference on Machine Learning, 2019

Subspace methods for multi-channel sum-of-exponentials common dynamics estimation.

Ivan Markovsky, Tianxiang Liu, Akiko Takeda

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018

DC formulations and algorithms for sparse optimization problems.

Jun-ya Gotoh, Akiko Takeda, Katsuya Tono

Math. Program., 2018

Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization.

Shinji Yamada, Akiko Takeda

J. Glob. Optim., 2018

Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems.

Naoki Ito, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Kim-Chuan Toh

J. Glob. Optim., 2018

Improving cash logistics in bank branches by coupling machine learning and robust optimization.

Jorge López Lázaro, Álvaro Barbero Jiménez, Akiko Takeda

Expert Syst. Appl., 2018

Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers.

Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda

CoRR, 2018

Nonconvex Optimization for Regression with Fairness Constraints.

Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao

Proceedings of the 35th International Conference on Machine Learning, 2018

Robust Densest Subgraph Discovery.

Atsushi Miyauchi, Akiko Takeda

Proceedings of the IEEE International Conference on Data Mining, 2018

2017

Exact Semidefinite Programming Relaxations with Truncated Moment Matrix for Binary Polynomial Optimization Problems.

Shinsaku Sakaue, Akiko Takeda, Sunyoung Kim, Naoki Ito

SIAM J. Optim., 2017

Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem.

Satoru Adachi, Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda

SIAM J. Optim., 2017

Robustness of learning algorithms using hinge loss with outlier indicators.

Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda

Neural Networks, 2017

DC Algorithm for Extended Robust Support Vector Machine.

Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori

Neural Comput., 2017

A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification.

Naoki Ito, Akiko Takeda, Kim-Chuan Toh

J. Mach. Learn. Res., 2017

Breakdown Point of Robust Support Vector Machines.

Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda

Entropy, 2017

Optimistic Robust Optimization With Applications To Machine Learning.

Matthew Norton, Akiko Takeda, Alexander Mafusalov

CoRR, 2017

Boosting the kernelized shapelets: Theory and algorithms for local features.

Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda

CoRR, 2017

Household energy consumption prediction by feature selection of lifestyle data.

Kosuke Nishida, Akiko Takeda, Satoru Iwata, Mariko Kiho, Isao Nakayama

Proceedings of the 2017 IEEE International Conference on Smart Grid Communications, 2017

Trimmed Density Ratio Estimation.

Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Position-based Multiple-play Bandit Problem with Unknown Position Bias.

Junpei Komiyama, Junya Honda, Akiko Takeda

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Solving Generalized CDT Problems via Two-Parameter Eigenvalues.

Shinsaku Sakaue, Yuji Nakatsukasa, Akiko Takeda, Satoru Iwata

SIAM J. Optim., 2016

2015

Cyber Security Analysis of Power Networks by Hypergraph Cut Algorithms.

Yutaro Yamaguchi, Anna Ogawa, Akiko Takeda, Satoru Iwata

IEEE Trans. Smart Grid, 2015

Computing the Signed Distance Between Overlapping Ellipsoids.

Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda

SIAM J. Optim., 2015

Geometric intuition and algorithms for Ev-SVM.

Álvaro Barbero Jiménez, Akiko Takeda, Jorge López Lázaro

J. Mach. Learn. Res., 2015

Optimizing over coherent risk measures and non-convexities: a robust mixed integer optimization approach.

Dimitris Bertsimas, Akiko Takeda

Comput. Optim. Appl., 2015

Outlier detection at the transcriptome-proteome interface.

Yawwani Gunawardana, Shuhei Fujiwara, Akiko Takeda, Jeongmin Woo, Christopher H. Woelk, Mahesan Niranjan

Bioinform., 2015

Robust Cost Sensitive Support Vector Machine.

Shuichi Katsumata, Akiko Takeda

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Using financial risk measures for analyzing generalization performance of machine learning models.

Akiko Takeda, Takafumi Kanamori

Neural Networks, 2014

Extended Robust Support Vector Machine Based on Financial Risk Minimization.

Akiko Takeda, Shuhei Fujiwara, Takafumi Kanamori

Neural Comput., 2014

Breakdown Point of Robust Support Vector Machine.

Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda

CoRR, 2014

Numerical study of learning algorithms on Stiefel manifold.

Takafumi Kanamori, Akiko Takeda

Comput. Manag. Sci., 2014

Interaction between financial risk measures and machine learning methods.

Jun-ya Gotoh, Akiko Takeda, Rei Yamamoto

Comput. Manag. Sci., 2014

Exact SVM training by Wolfe's minimum norm point algorithm.

Masashi Kitamura, Akiko Takeda, Satoru Iwata

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2014

Memory-efficient large-scale linear support vector machine.

Abdullah Alrajeh, Akiko Takeda, Mahesan Niranjan

Proceedings of the Seventh International Conference on Machine Vision, 2014

Global Optimization Methods for Extended Fisher Discriminant Analysis.

Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

A Unified Classification Model Based on Robust Optimization.

Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori

Neural Comput., 2013

Conjugate relation between loss functions and uncertainty sets in classification problems.

Takafumi Kanamori, Akiko Takeda, Taiji Suzuki

J. Mach. Learn. Res., 2013

Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios.

Akiko Takeda, Mahesan Niranjan, Jun-ya Gotoh, Yoshinobu Kawahara

Comput. Manag. Sci., 2013

Convex hull pricing for demand response in electricity markets.

Naoki Ito, Akiko Takeda, Toru Namerikawa

Proceedings of the IEEE Fourth International Conference on Smart Grid Communications, 2013

Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering.

Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty.

Takafumi Kanamori, Akiko Takeda

J. Optim. Theory Appl., 2012

A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems.

Takafumi Kanamori, Akiko Takeda, Taiji Suzuki

Proceedings of the COLT 2012, 2012

Minimizing loss probability bounds for portfolio selection.

Jun-ya Gotoh, Akiko Takeda

Eur. J. Oper. Res., 2012

Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning.

Takafumi Kanamori, Akiko Takeda

Proceedings of the Neural Information Processing - 19th International Conference, 2012

A Unified Robust Classification Model.

Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

On the role of norm constraints in portfolio selection.

Jun-ya Gotoh, Akiko Takeda

Comput. Manag. Sci., 2011

2010

A relaxation algorithm with a probabilistic guarantee for robust deviation optimization.

Akiko Takeda, Shunsuke Taguchi, Tsutomu Tanaka

Comput. Optim. Appl., 2010

2009

On Generalization Performance and Non-Convex Optimization of Extended <i>nu</i>-Support Vector Machine.

Akiko Takeda, Masashi Sugiyama

New Gener. Comput., 2009

Generalization performance of nu-support vector classifier based on conditional value-at-risk minimization.

Akiko Takeda

Neurocomputing, 2009

A robust approach based on conditional value-at-risk measure to statistical learning problems.

Akiko Takeda, Takafumi Kanamori

Eur. J. Oper. Res., 2009

2008

Conditional minimum volume ellipsoid with application to multiclass discrimination.

Jun-ya Gotoh, Akiko Takeda

Comput. Optim. Appl., 2008

<i>nu</i>-support vector machine as conditional value-at-risk minimization.

Akiko Takeda, Masashi Sugiyama

Proceedings of the Machine Learning, 2008

2007

Dynamic Enumeration of All Mixed Cells.

Tomohiko Mizutani, Akiko Takeda, Masakazu Kojima

Discret. Comput. Geom., 2007

2004

PHoM - a Polyhedral Homotopy Continuation Method for Polynomial Systems.

Takayuki Gunji, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Katsuki Fujisawa, Tomohiko Mizutani

Computing, 2004

High Performance Grid and Cluster Computing for Some Optimization Problems.

Katsuki Fujisawa, Masakazu Kojima, Akiko Takeda, Makoto Yamashita

Proceedings of the 2004 Symposium on Applications and the Internet Workshops (SAINT 2004 Workshops), 2004

2002

Parallel Implementation of Successive Convex Relaxation Methods for Quadratic Optimization Problems.

Akiko Takeda, Katsuki Fujisawa, Yusuke Fukaya, Masakazu Kojima

J. Glob. Optim., 2002

2001

Complexity Analysis of Successive Convex Relaxation Methods for Nonconvex Sets.

Masakazu Kojima, Akiko Takeda

Math. Oper. Res., 2001

On measuring the inefficiency with the inner-product norm in data envelopment analysis.

Akiko Takeda, Hisakazu Nishino

Eur. J. Oper. Res., 2001