On measuring the inefficiency with the inner-product norm in data envelopment analysis. - Akiko Takeda; Hisakazu Nishino (Eur. J. Oper. Res., 2001)

Complexity Analysis of Successive Convex Relaxation Methods for Nonconvex Sets. - Masakazu Kojima; Akiko Takeda (Math. Oper. Res., 2001)

Parallel Implementation of Successive Convex Relaxation Methods for Quadratic Optimization Problems. - Akiko Takeda; Katsuki Fujisawa; Yusuke Fukaya; Masakazu Kojima (J. Glob. Optim., 2002)

High Performance Grid and Cluster Computing for Some Optimization Problems. - Katsuki Fujisawa; Masakazu Kojima; Akiko Takeda; Makoto Yamashita (SAINT Workshops, 2004)

PHoM - a Polyhedral Homotopy Continuation Method for Polynomial Systems. - Takayuki Gunji; Sunyoung Kim; Masakazu Kojima; Akiko Takeda; Katsuki Fujisawa; Tomohiko Mizutani (Computing, 2004)

Dynamic Enumeration of All Mixed Cells. - Tomohiko Mizutani; Akiko Takeda; Masakazu Kojima (Discret. Comput. Geom., 2007)

nu-support vector machine as conditional value-at-risk minimization. - Akiko Takeda; Masashi Sugiyama (ICML, 2008)

Conditional minimum volume ellipsoid with application to multiclass discrimination. - Jun-ya Gotoh; Akiko Takeda (Comput. Optim. Appl., 2008)

A robust approach based on conditional value-at-risk measure to statistical learning problems. - Akiko Takeda; Takafumi Kanamori (Eur. J. Oper. Res., 2009)

Generalization performance of nu-support vector classifier based on conditional value-at-risk minimization. - Akiko Takeda (Neurocomputing, 2009)

On Generalization Performance and Non-Convex Optimization of Extended nu-Support Vector Machine. - Akiko Takeda; Masashi Sugiyama (New Gener. Comput., 2009)

A relaxation algorithm with a probabilistic guarantee for robust deviation optimization. - Akiko Takeda; Shunsuke Taguchi; Tsutomu Tanaka (Comput. Optim. Appl., 2010)

On the role of norm constraints in portfolio selection. - Jun-ya Gotoh; Akiko Takeda (Comput. Manag. Sci., 2011)

A Unified Robust Classification Model. - Akiko Takeda; Hiroyuki Mitsugi; Takafumi Kanamori (ICML, 2012)

Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning. - Takafumi Kanamori; Akiko Takeda (ICONIP (1), 2012)

Minimizing loss probability bounds for portfolio selection. - Jun-ya Gotoh; Akiko Takeda (Eur. J. Oper. Res., 2012)

A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. - Takafumi Kanamori; Akiko Takeda; Taiji Suzuki (COLT, 2012)

Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty. - Takafumi Kanamori; Akiko Takeda (J. Optim. Theory Appl., 2012)

Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. - Shinichi Nakajima; Akiko Takeda; S. Derin Babacan; Masashi Sugiyama; Ichiro Takeuchi (NIPS, 2013)

Convex hull pricing for demand response in electricity markets. - Naoki Ito; Akiko Takeda; Toru Namerikawa (SmartGridComm, 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)

Conjugate relation between loss functions and uncertainty sets in classification problems. - Takafumi Kanamori; Akiko Takeda; Taiji Suzuki (J. Mach. Learn. Res., 2013)

A Unified Classification Model Based on Robust Optimization. - Akiko Takeda; Hiroyuki Mitsugi; Takafumi Kanamori (Neural Comput., 2013)

Global Optimization Methods for Extended Fisher Discriminant Analysis. - Satoru Iwata 001; Yuji Nakatsukasa; Akiko Takeda (AISTATS, 2014)

Memory-efficient large-scale linear support vector machine. - Abdullah Alrajeh; Akiko Takeda; Mahesan Niranjan (ICMV, 2014)

Exact SVM training by Wolfe's minimum norm point algorithm. - Masashi Kitamura; Akiko Takeda; Satoru Iwata 001 (MLSP, 2014)

Interaction between financial risk measures and machine learning methods. - Jun-ya Gotoh; Akiko Takeda; Rei Yamamoto (Comput. Manag. Sci., 2014)

Numerical study of learning algorithms on Stiefel manifold. - Takafumi Kanamori; Akiko Takeda (Comput. Manag. Sci., 2014)

Breakdown Point of Robust Support Vector Machine. - Takafumi Kanamori; Shuhei Fujiwara; Akiko Takeda (CoRR, 2014)

Extended Robust Support Vector Machine Based on Financial Risk Minimization. - Akiko Takeda; Shuhei Fujiwara; Takafumi Kanamori (Neural Comput., 2014)

Using financial risk measures for analyzing generalization performance of machine learning models. - Akiko Takeda; Takafumi Kanamori (Neural Networks, 2014)

Robust Cost Sensitive Support Vector Machine. - Shuichi Katsumata; Akiko Takeda (AISTATS, 2015)

Outlier detection at the transcriptome-proteome interface. - Yawwani Gunawardana; Shuhei Fujiwara; Akiko Takeda; Jeongmin Woo; Christopher H. Woelk; Mahesan Niranjan (Bioinform., 2015)

Optimizing over coherent risk measures and non-convexities: a robust mixed integer optimization approach. - Dimitris Bertsimas; Akiko Takeda (Comput. Optim. Appl., 2015)

Geometric intuition and algorithms for Ev-SVM. - Álvaro Barbero Jiménez; Akiko Takeda; Jorge López Lázaro (J. Mach. Learn. Res., 2015)

Computing the Signed Distance Between Overlapping Ellipsoids. - Satoru Iwata 001; Yuji Nakatsukasa; Akiko Takeda (SIAM J. Optim., 2015)

Cyber Security Analysis of Power Networks by Hypergraph Cut Algorithms. - Yutaro Yamaguchi 001; Anna Ogawa; Akiko Takeda; Satoru Iwata 001 (IEEE Trans. Smart Grid, 2015)

Solving Generalized CDT Problems via Two-Parameter Eigenvalues. - Shinsaku Sakaue; Yuji Nakatsukasa; Akiko Takeda; Satoru Iwata 001 (SIAM J. Optim., 2016)

Position-based Multiple-play Bandit Problem with Unknown Position Bias. - Junpei Komiyama; Junya Honda; Akiko Takeda (NIPS, 2017)

Trimmed Density Ratio Estimation. - Song Liu 002; Akiko Takeda; Taiji Suzuki; Kenji Fukumizu (NIPS, 2017)

Household energy consumption prediction by feature selection of lifestyle data. - Kosuke Nishida; Akiko Takeda; Satoru Iwata 001; Mariko Kiho; Isao Nakayama (SmartGridComm, 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)

Optimistic Robust Optimization With Applications To Machine Learning. - Matthew Norton 001; Akiko Takeda; Alexander Mafusalov (CoRR, 2017)

Breakdown Point of Robust Support Vector Machines. - Takafumi Kanamori; Shuhei Fujiwara; Akiko Takeda (Entropy, 2017)

A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification. - Naoki Ito; Akiko Takeda; Kim-Chuan Toh (J. Mach. Learn. Res., 2017)

DC Algorithm for Extended Robust Support Vector Machine. - Shuhei Fujiwara; Akiko Takeda; Takafumi Kanamori (Neural Comput., 2017)

Robustness of learning algorithms using hinge loss with outlier indicators. - Takafumi Kanamori; Shuhei Fujiwara; Akiko Takeda (Neural Networks, 2017)

Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem. - Satoru Adachi; Satoru Iwata 001; Yuji Nakatsukasa; Akiko Takeda (SIAM J. Optim., 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)

Robust Densest Subgraph Discovery. - Atsushi Miyauchi 001; Akiko Takeda (ICDM, 2018)

Nonconvex Optimization for Regression with Fairness Constraints. - Junpei Komiyama; Akiko Takeda; Junya Honda; Hajime Shimao (ICML, 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)

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)

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)

Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization. - Shinji Yamada; Akiko Takeda (J. Glob. Optim., 2018)

DC formulations and algorithms for sparse optimization problems. - Jun-ya Gotoh; Akiko Takeda; Katsuya Tono (Math. Program., 2018)

Subspace methods for multi-channel sum-of-exponentials common dynamics estimation. - Ivan Markovsky; Tianxiang Liu; Akiko Takeda (CDC, 2019)

Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization. - Michael R. Metel; Akiko Takeda (ICML, 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)

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)

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)

Convex Fairness Constrained Model Using Causal Effect Estimators. - Hikaru Ogura; Akiko Takeda (WWW (Companion Volume), 2020)

Construction Methods of the Nearest Positive System. - Kazuhiro Sato; Akiko Takeda (IEEE Control. Syst. Lett., 2020)

Controllability Maximization of Large-Scale Systems Using Projected Gradient Method. - Kazuhiro Sato; Akiko Takeda (IEEE Control. Syst. 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)

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)

Robust Bayesian model selection for variable clustering with the Gaussian graphical model. - Daniel Andrade; Akiko Takeda; Kenji Fukumizu (Stat. Comput., 2020)

Generalized Subdifferentials of Spectral Functions over Euclidean Jordan Algebras. - Bruno F. Lourenço; Akiko Takeda (SIAM J. Optim., 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)

Data-Driven Structured Noise Filtering via Common Dynamics Estimation. - Ivan Markovsky; Tianxiang Liu; Akiko Takeda (IEEE Trans. Signal Process., 2020)

A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion. - Naoki Marumo; Atsushi Miyauchi 001; Akiko Takeda; Akira Tanaka (CIKM, 2021)

A Gradient Method for Multilevel Optimization. - Ryo Sato; Mirai Tanaka; Akiko Takeda (NeurIPS, 2021)

BODAME: Bilevel Optimization for Defense Against Model Extraction. - Yuto Mori; Atsushi Nitanda; Akiko Takeda (CoRR, 2021)

Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization. - Michael R. Metel; Akiko Takeda (J. Mach. Learn. Res., 2021)

On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization. - Takayuki Okuno; Akiko Takeda; Akihiro Kawana; Motokazu Watanabe (J. Mach. Learn. Res., 2021)

Primal-dual subgradient method for constrained convex optimization problems. - Michael R. Metel; Akiko Takeda (Optim. Lett., 2021)

Single Loop Gaussian Homotopy Method for Non-convex Optimization. - Hidenori Iwakiri; Yuhang Wang; Shinji Ito; Akiko Takeda (NeurIPS, 2022)

An inexact successive quadratic approximation method for a class of difference-of-convex optimization problems. - Tianxiang Liu; Akiko Takeda (Comput. Optim. Appl., 2022)

Perturbed Iterate SGD for Lipschitz Continuous Loss Functions. - Michael R. Metel; Akiko Takeda (J. Optim. Theory Appl., 2022)

Convexification with Bounded Gap for Randomly Projected Quadratic Optimization. - Terunari Fuji; Pierre-Louis Poirion; Akiko Takeda (SIAM J. Optim., 2022)

Sequential Quadratic Optimization for Nonlinear Optimization Problems on Riemannian Manifolds. - Mitsuaki Obara; Takayuki Okuno; Akiko Takeda (SIAM J. Optim., 2022)

Robust Gaussian process regression with the trimmed marginal likelihood. - Daniel Andrade; Akiko Takeda (UAI, 2023)

Majorization-minimization-based Levenberg-Marquardt method for constrained nonlinear least squares. - Naoki Marumo; Takayuki Okuno; Akiko Takeda (Comput. Optim. Appl., 2023)

A study on modularity density maximization: Column generation acceleration and computational complexity analysis. - Issey Sukeda; Atsushi Miyauchi 001; Akiko Takeda (Eur. J. Oper. Res., 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., 2023)

Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints. - Tianxiang Liu; Ting Kei Pong; Akiko Takeda (Comput. Optim. Appl., 2023)

SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining. - Andi Han; Jiaxiang Li; Wei Huang; Mingyi Hong 001; Akiko Takeda; Pratik Kumar Jawanpuria; Bamdev Mishra (NeurIPS, 2024)

A Framework for Bilevel Optimization on Riemannian Manifolds. - Andi Han; Bamdev Mishra; Pratik Kumar Jawanpuria; Akiko Takeda (NeurIPS, 2024)

Initial Placement for Fruchterman-Reingold Force Model with Coordinate Newton Direction. - Hiroki Hamaguchi; Naoki Marumo; Akiko Takeda (CoRR, 2024)

Parameter-Free Accelerated Gradient Descent for Nonconvex Minimization. - Naoki Marumo; Akiko Takeda (SIAM J. Optim., 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., 2024)

Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions. - Yuya Hikima; Akiko Takeda (AAAI, 2025)

The Adaptive Complexity of Finding a Stationary Point. - Huanjian Zhou; Andi Han; Akiko Takeda; Masashi Sugiyama (COLT, 2025)

Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization. - Rei Higuchi; Pierre-Louis Poirion; Akiko Takeda (ICLR, 2025)

Stochastic approach for price optimization problems with decision-dependent uncertainty. - Yuya Hikima; Akiko Takeda (Eur. J. Oper. Res., 2025)

A Four-Operator Splitting Algorithm for Nonconvex and Nonsmooth Optimization. - Jan Harold Alcantara; Ching-pei Lee; Akiko Takeda (SIAM J. Optim., 2025)

Approximate bregman proximal gradient algorithm for relatively smooth nonconvex optimization. - Shota Takahashi; Akiko Takeda (Comput. Optim. Appl., 2025)

Zeroth-Order Random Subspace Algorithm for Non-smooth Convex Optimization. - Ryota Nozawa; Pierre-Louis Poirion; Akiko Takeda (J. Optim. Theory Appl., 2025)

Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method. - Andi Han; Pierre-Louis Poirion; Akiko Takeda (CoRR, 2025)

On the Role of Label Noise in the Feature Learning Process. - Andi Han; Wei Huang 034; Zhanpeng Zhou; Gang Niu 001; Wuyang Chen 001; Junchi Yan; Akiko Takeda; Taiji Suzuki (CoRR, 2025)

Modified K-means Algorithm with Local Optimality Guarantees. - Mingyi Li; Michael R. Metel; Akiko Takeda (CoRR, 2025)

Unified smoothing approach for best hyperparameter selection problem using a bilevel optimization strategy. - Jan Harold Alcantara; Chieu Thanh Nguyen; Takayuki Okuno; Akiko Takeda; Jein-Shan Chen (Math. Program., 2025)

Universal heavy-ball method for nonconvex optimization under Hölder continuous Hessians. - Naoki Marumo; Akiko Takeda (Math. Program., 2025)

Accelerated-gradient-based generalized Levenberg-Marquardt method with oracle complexity bound and local quadratic convergence. - Naoki Marumo; Takayuki Okuno; Akiko Takeda (Math. Program., 2025)

Univariate Representations of Solutions to Generic Polynomial Complementarity Problems. - Vu Trung Hieu; Alfredo Noel Iusem; Paul Hugo Schmölling; Akiko Takeda (J. Optim. Theory Appl., 2025)