A. Takeda

发表

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

Ting Kei Pong, Tianxiang Liu, A. Takeda, 2018, Mathematical Programming.

Parameter-free accelerated gradient descent for nonconvex minimization

Naoki Marumo, A. Takeda, 2022, 2212.06410.

Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization

Shinji Yamada, A. Takeda, 2018, Journal of Global Optimization.

Numerical study of learning algorithms on Stiefel manifold

T. Kanamori, A. Takeda, 2013, Computational Management Science.

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

K. Fukumizu, A. Takeda, Daniel Andrade, 2019, Statistics and Computing.

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

Takayuki Okuno, Naoki Marumo, A. Takeda, 2020, Computational Optimization and Applications.

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

T. Kanamori, A. Takeda, 2011, Journal of Optimization Theory and Applications.

Random projections of linear and semidefinite problems with linear inequalities

Bruno F. Lourenço, Bruno F. Lourencco, Pierre-Louis Poirion, 2020, Linear Algebra and its Applications.

Algorithm 996

Kim-Chuan Toh, Akiko Takeda, Masakazu Kojima, 2018, ACM Trans. Math. Softw..

Generalized Subdifferentials of Spectral Functions over Euclidean Jordan Algebras

Bruno F. Lourenço, Akiko Takeda, A. Takeda, 2019, SIAM J. Optim..

Extended Robust Support Vector Machine Based on Financial Risk Minimization

Takafumi Kanamori, Akiko Takeda, Shuhei Fujiwara, 2014, Neural Computation.

Interaction between financial risk measures and machine learning methods

Akiko Takeda, Rei Yamamoto, Jun-ya Gotoh, 2014, Comput. Manag. Sci..

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

Takafumi Kanamori, Akiko Takeda, Taiji Suzuki, 2012, COLT.

Minimizing loss probability bounds for portfolio selection

Akiko Takeda, Jun-ya Gotoh, J. Gotoh, 2012, Eur. J. Oper. Res..

Numerical study of learning algorithms on Stiefel manifold

Takafumi Kanamori, Akiko Takeda, T. Kanamori, 2014, Comput. Manag. Sci..

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

Takafumi Kanamori, Akiko Takeda, T. Kanamori, 2014, Neural Networks.

ν-support vector machine as conditional value-at-risk minimization

Akiko Takeda, Masashi Sugiyama, Masashi Sugiyama, 2008, ICML '08.

A Study on Modularity Density Maximization: Column Generation Acceleration and Computational Complexity Analysis

Atsushi Miyauchi, Issey Sukeda, Akiko Takeda, 2022, Eur. J. Oper. Res..

Outlier detection at the transcriptome-proteome interface

Akiko Takeda, Mahesan Niranjan, Yawwani Gunawardana, 2015, Bioinform..

Breakdown Point of Robust Support Vector Machines

Takafumi Kanamori, Akiko Takeda, Shuhei Fujiwara, 2017, Entropy.

On the role of norm constraints in portfolio selection

Akiko Takeda, Jun-ya Gotoh, J. Gotoh, 2011, Comput. Manag. Sci..

Interaction between financial risk measures and machine learning methods

J. Gotoh, Akiko Takeda, Rei Yamamoto, 2013, Computational Management Science.

Successive Convex Relaxation Methods for Nonconvex Quadratic Optimization Problems

Akiko Takeda, A. Takeda, 2000 .

Complexity Analysis of Successive Convex Relaxation Methods for Nonconvex Sets

Akiko Takeda, Masakazu Kojima, M. Kojima, 2001, Math. Oper. Res..

B-347 Towards the Implementation of Successive Convex Relaxation Method for Noncon- vex Quadratic Optimization Problems

Akiko Takeda, Mituhiro Fukuda, Mituhiro Fukuda, 1999 .

Parallel Implementation of Successive Convex Relaxation Methods for Quadratic Optimization Problems

Akiko Takeda, Masakazu Kojima, Katsuki Fujisawa, 2002, J. Glob. Optim..

Successive Convex Relaxation Approach to Bilevel Quadratic Optimization Problems

Akiko Takeda, Masakazu Kojima, M. Kojima, 2001 .

Perturbed Iterate SGD for Lipschitz Continuous Loss Functions

Michael R. Metel, Akiko Takeda, A. Takeda, 2020, Journal of Optimization Theory and Applications.

Bilevel Optimization of Regularization Hyperparameters in Machine Learning

Akiko Takeda, Takayuki Okuno, Takayuki Okuno, 2020 .

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

Akiko Takeda, Yoshinobu Kawahara, Mahesan Niranjan, 2012, Comput. Manag. Sci..

Robust portfolio techniques for mitigating the fragility of CVaR minimization and generalization to coherent risk measures

Akiko Takeda, J. Gotoh, K. Shinozaki, 2013 .

Conditional minimum volume ellipsoid with application to multiclass discrimination

Akiko Takeda, Jun-ya Gotoh, J. Gotoh, 2008, Comput. Optim. Appl..

Scalable Optimization Approach of Optimal PV Sizing Problem for Minimizing Network Loss and Inverter Loss

Akiko Takeda, Hiromitsu Ohmori, Shunnosuke Ikeda, 2020, Transactions of the Institute of Systems, Control and Information Engineers.

Constrained Levenberg-Marquardt Method with Global Complexity Bound

Akiko Takeda, Takayuki Okuno, Naoki Marumo, 2020 .

Universal heavy-ball method for nonconvex optimization under H\"older continuous Hessians

Naoki Marumo, A. Takeda, 2023, 2303.01073.

Nonconvex Optimization for Regression with Fairness Constraints

Akiko Takeda, Junpei Komiyama, Junya Honda, 2018, ICML.

ENUMERATION OF ALL SOLUTIONS OF A COMBINATORIAL LINEAR INEQUALITY SYSTEM ARISING FROM THE POLYHEDRAL HOMOTOPY CONTINUATION METHOD

Akiko Takeda, Masakazu Kojima, Katsuki Fujisawa, 2002 .

Interior-point methods for second-order stationary points of nonlinear semideﬁnite optimization problems using negative curvature

Akiko Takeda, Takayuki Okuno, Shun Arahata, 2021 .

Construction Methods of the Nearest Positive System

Kazuhiro Sato, Akiko Takeda, Kazuhiro Sato, 2020, IEEE Control Systems Letters.

Robustness of learning algorithms using hinge loss with outlier indicators

Takafumi Kanamori, Akiko Takeda, Shuhei Fujiwara, 2017, Neural Networks.

DC Algorithm for Extended Robust Support Vector Machine

Takafumi Kanamori, Akiko Takeda, Shuhei Fujiwara, 2017, Neural Computation.

Hyperparameter Learning via Bilevel Nonsmooth Optimization

Akiko Takeda, Takayuki Okuno, Akihiro Kawana, 2018 .

Economic and environmental analysis of photovoltaic energy systems via robust optimization

A. Takeda, Shimpei Okido, 2013, Energy Systems.

Economic and environmental analysis of photovoltaic energy systems via robust optimization

Akiko Takeda, Shimpei Okido, A. Takeda, 2013, Energy Systems.

Cyber security analysis of power networks by hypergraph cut algorithms

Akiko Takeda, Satoru Iwata, Yutaro Yamaguchi, 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

PHoM – a Polyhedral Homotopy Continuation Method for Polynomial Systems

Akiko Takeda, Masakazu Kojima, Sunyoung Kim, 2004, Computing.

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

Akiko Takeda, Jorge López Lázaro, Álvaro Barbero Jiménez, 2018, Expert Syst. Appl..

Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems

Taiji Suzuki, Ray Kawai, A. Takeda, 2022, 2203.10215.

Computing the Signed Distance Between Overlapping Ellipsoids

Akiko Takeda, Satoru Iwata, Yuji Nakatsukasa, 2015, SIAM J. Optim..

A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification

Kim-Chuan Toh, Akiko Takeda, Naoki Ito, 2017, J. Mach. Learn. Res..

A linear classification model based on conditional geometric score

Akiko Takeda, Jun-ya Gotoh, J. Gotoh, 2004 .

A refined convergence analysis of pDCAe\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {pDCA}_{e}$$\end{document

Akiko Takeda, Ting Kei Pong, Tianxiang Liu, 2018, Computational Optimization and Applications.

A refined convergence analysis of pDCA$_e$ with applications to simultaneous sparse recovery and outlier detection

Akiko Takeda, Ting Kei Pong, Akiko Takeda, 2018 .

Convex hull pricing for demand response in electricity markets

Akiko Takeda, Toru Namerikawa, Naoki Ito, 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

A New Dynamic Pricing Model based on Convex Hull Pricing

Akiko Takeda, Toru Namerikawa, Naoki Ito, 2013, 1310.4962.

A New Dynamic Pricing Method Based on

T. Namerikawa, A. Takeda, N. Ito, 2014 .

Accelerated-gradient-based generalized Levenberg--Marquardt method with oracle complexity bound and local quadratic convergence

Takayuki Okuno, Naoki Marumo, Akiko Takeda, 2022, 2204.12016.

Optimal Sizing of Energy Storage Systems for the Energy Procurement Problem in Multi-Period Markets under Uncertainties

Akiko Takeda, Ryusuke Konishi, Akiko Takeda, 2018 .

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

Takayuki Okuno, A. Takeda, S. Arahata, 2021, Computational Optimization and Applications.

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

Akiko Takeda, Michael R. Metel, A. Takeda, 2019, J. Mach. Learn. Res..

Stable Linear System Identification with Prior Knowledge by Elastic Riemannian Sequential Quadratic Optimization

Akiko Takeda, Mitsuaki Obara, Takayuki Okuno, 2021, 2112.14043.

DC formulations and algorithms for sparse optimization problems

Akiko Takeda, Jun-ya Gotoh, Katsuya Tono, 2017, Mathematical Programming.

Controllability Maximization of Large-Scale Systems Using Projected Gradient Method

Akiko Takeda, Kazuhiro Sato, Kazuhiro Sato, 2020, IEEE Control Systems Letters.

Stochastic Approach for Price Optimization Problems with Decision-dependent Uncertainty

Yuya Hikima, A. Takeda, 2023, 2307.00492.

Trimmed Density Ratio Estimation

Akiko Takeda, Kenji Fukumizu, Taiji Suzuki, 2017, NIPS.

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

Akiko Takeda, Ting Kei Pong, Tianxiang Liu, 2017, Mathematical Programming.

Single Loop Gaussian Homotopy Method for Non-convex Optimization

Shinji Ito, Akiko Takeda, H. Iwakiri, 2022, NeurIPS.

Convex Fairness Constrained Model Using Causal Effect Estimators

Akiko Takeda, Hikaru Ogura, A. Takeda, 2020, WWW.

Memory-efficient large-scale linear support vector machine

Akiko Takeda, Mahesan Niranjan, Abdullah Alrajeh, 2015, Other Conferences.

Exact SVM training by Wolfe's minimum norm point algorithm

Akiko Takeda, Satoru Iwata, Masashi Kitamura, 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

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

Akiko Takeda, Michael R. Metel, A. Takeda, 2019, ICML.

Adjustable Robust Optimization Models for Nonlinear Multi-Period Optimization ∗

R. Tütüncü, S. Taguchi, A. Takeda, 2004 .

Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem

Akiko Takeda, Satoru Iwata, Yuji Nakatsukasa, 2017, SIAM J. Optim..

DC formulations and algorithms for sparse optimization problems

J. Gotoh, Akiko Takeda, Katsuya Tono, 2017, Mathematical Programming.

A Gradient Method for Multilevel Optimization pdf

Akiko Takeda, Mirai Tanaka, Ryo Sato, 2021, NeurIPS.

On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization

Takayuki Okuno, A. Takeda, Akihiro Kawana, 2018, J. Mach. Learn. Res..

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

Takafumi Kanamori, Akiko Takeda, T. Kanamori, 2012, ICONIP.

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

Ting Kei Pong, Ivan Markovsky, Akiko Takeda, 2019, SIAM J. Matrix Anal. Appl..

Convexification with Bounded Gap for Randomly Projected Quadratic Optimization

Akiko Takeda, Pierre-Louis Poirion, Terunari Fuji, 2021, SIAM J. Optim..

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

Akiko Takeda, Junpei Komiyama, Junya Honda, 2017, NIPS.

Robust Densest Subgraph Discovery

Akiko Takeda, Atsushi Miyauchi, Atsushi Miyauchi, 2018, 2018 IEEE International Conference on Data Mining (ICDM).

Riemannian Levenberg-Marquardt Method with Global and Local Convergence Properties

Takayuki Okuno, Sho Adachi, A. Takeda, 2022, 2210.00253.

Theory and Algorithms for Shapelet-Based Multiple-Instance Learning

E. Takimoto, Shuji Yamamoto, D. Suehiro, 2020, Neural Computation.

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

Akiko Takeda, Eiji Takimoto, Kohei Hatano, 2018, ArXiv.

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

Akiko Takeda, Eiji Takimoto, Kohei Hatano, 2017, ArXiv.

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

Takafumi Kanamori, Akiko Takeda, T. Kanamori, 2009, Eur. J. Oper. Res..

BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems with Binary, Box and Complementarity Constraints

M. Kojima, K. Toh, Sunyoung Kim, 2018, 1804.00761.

Support Vector Machine Based on Conditional Value-at-Risk Minimization

A. Takeda, 2007 .

BODAME: Bilevel Optimization for Defense Against Model Extraction

Atsushi Nitanda, A. Takeda, Y. Mori, 2021, ArXiv.

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

Akiko Takeda, Sunyoung Kim, Naoki Ito, 2017, SIAM J. Optim..

Data-Driven Structured Noise Filtering via Common Dynamics Estimation

Akiko Takeda, Ivan Markovsky, Tianxiang Liu, 2020, IEEE Transactions on Signal Processing.

Global Optimization Methods for Extended Fisher Discriminant Analysis

Akiko Takeda, Satoru Iwata, Yuji Nakatsukasa, 2014, AISTATS.

Adjustable Robust Optimization Models for a Nonlinear Two-Period System

R. Tütüncü, Akiko Takeda, S. Taguchi, 2008 .

Accelerated-gradient-based generalized Levenberg--Marquardt method with oracle complexity bound and local quadratic convergence

Takayuki Okuno, Naoki Marumo, A. Takeda, 2022, 2204.12016.

Optimal sizing of photovoltaic systems for loss minimization in distribution network

Hiromitsu Ohmori, Shunnosuke Ikeda, Akiko Takeda, 2018, 2018 SICE International Symposium on Control Systems (SICE ISCS).

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

Akiko Takeda, Atsushi Miyauchi, Naoki Marumo, 2021, CIKM.

Efficient DC Algorithm for Constrained Sparse Optimization

Akiko Takeda, Jun-ya Gotoh, Katsuya Tono, 2017, 1701.08498.

A Fast Unified Classification Algorithm Based on Accelerated Proximal Gradient Method

K. Toh, Akiko Takeda, Metr, 2015 .

$ \nu$ -Support Vector Machine as Conditional Value-at-Risk Minimization

Masashi Sugiyama, A. Takeda, 2011 .

Primal-dual subgradient method for constrained convex optimization problems

Akiko Takeda, Michael R. Metel, A. Takeda, 2021, Optim. Lett..

Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization

Akiko Takeda, Shinji Yamada, Shinji Yamada, 2018, Journal of Global Optimization.

Optimistic Robust Optimization With Applications To Machine Learning pdf

Akiko Takeda, Matthew Norton, Alexander Mafusalov, 2017, ArXiv.

Support vector regression as conditional value-at-risk minimization with application to financial time-series analysis

Akiko Takeda, J. Gotoh, Masashi Sugiama, 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

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

Akiko Takeda, Hisakazu Nishino, H. Nishino, 2001, Eur. J. Oper. Res..