Akiko Takeda - RankMe

126 papers A* 15A 4C 1Journal 95Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Convergence Analysis of Randomized Subspace Normalized SGD under Heavy-Tailed Noise. CoRR Gaku Omiya, Pierre-Louis Poirion, Akiko Takeda
2026	J	`jnl`	Fairness in Robust Unit Commitment Problem Considering Suppression of Renewable Energy. CoRR Ichiro Toyoshima, Pierre-Louis Poirion, Tomohide Yamazaki, Kota Yaguchi, Masayuki Kubota, Ryota Mizutani, Akiko Takeda
2026	J	`jnl`	Local Near-Quadratic Convergence of Riemannian Interior Point Methods. J. Optim. Theory Appl. Mitsuaki Obara, Takayuki Okuno, Akiko Takeda
2025	J	`jnl`	A Four-Operator Splitting Algorithm for Nonconvex and Nonsmooth Optimization. SIAM J. Optim. Jan Harold Alcantara, Ching-pei Lee, Akiko Takeda
2025	J	`jnl`	Accelerated-gradient-based generalized Levenberg-Marquardt method with oracle complexity bound and local quadratic convergence. Math. Program. Naoki Marumo, Takayuki Okuno, Akiko Takeda
2025	J	`jnl`	Approximate bregman proximal gradient algorithm for relatively smooth nonconvex optimization. Comput. Optim. Appl. Shota Takahashi, Akiko Takeda
2025	J	`jnl`	Computing local minimizers in polynomial optimization under genericity conditions. J. Glob. Optim. Vu Trung Hieu, Akiko Takeda
2025	A*	`conf`	Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method. ICML Andi Han, Pierre-Louis Poirion, Akiko Takeda
2025	J	`jnl`	Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method. CoRR Andi Han, Pierre-Louis Poirion, Akiko Takeda
2025	A*	`conf`	Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization. ICLR Rei Higuchi, Pierre-Louis Poirion, Akiko Takeda
2025	A*	`conf`	Modified K-means Algorithm with Local Optimality Guarantees. ICML Mingyi Li, Michael R. Metel, Akiko Takeda
2025	J	`jnl`	Modified K-means Algorithm with Local Optimality Guarantees. CoRR Mingyi Li, Michael R. Metel, Akiko Takeda
2025	A*	`conf`	On the Role of Label Noise in the Feature Learning Process. ICML Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki
2025	J	`jnl`	On the Role of Label Noise in the Feature Learning Process. CoRR Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki
2025	J	`jnl`	Sparse sub-gaussian random projections for semidefinite programming relaxations. J. Glob. Optim. Monse Guedes-Ayala, Pierre-Louis Poirion, Lars Schewe, Akiko Takeda
2025	J	`jnl`	Stochastic approach for price optimization problems with decision-dependent uncertainty. Eur. J. Oper. Res. Yuya Hikima, Akiko Takeda
2025	A*	`conf`	The Adaptive Complexity of Finding a Stationary Point. COLT Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama
2025	J	`jnl`	The Adaptive Complexity of Finding a Stationary Point. CoRR Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama
2025	J	`jnl`	Theoretical analysis of the randomized subspace regularized Newton method for non-convex optimization. Open J. Math. Optim. Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda
2025	J	`jnl`	Unified smoothing approach for best hyperparameter selection problem using a bilevel optimization strategy. Math. Program. Jan Harold Alcantara, Chieu Thanh Nguyen, Takayuki Okuno, Akiko Takeda, Jein-Shan Chen
2025	J	`jnl`	Univariate Representations of Solutions to Generic Polynomial Complementarity Problems. J. Optim. Theory Appl. Vu Trung Hieu, Alfredo Noel Iusem, Paul Hugo Schmölling, Akiko Takeda
2025	J	`jnl`	Universal heavy-ball method for nonconvex optimization under Hölder continuous Hessians. Math. Program. Naoki Marumo, Akiko Takeda
2025	A*	`conf`	Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions. AAAI Yuya Hikima, Akiko Takeda
2025	J	`jnl`	Zeroth-Order Random Subspace Algorithm for Non-smooth Convex Optimization. J. Optim. Theory Appl. Ryota Nozawa, Pierre-Louis Poirion, Akiko Takeda
2024	A*	`conf`	A Framework for Bilevel Optimization on Riemannian Manifolds. NeurIPS Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Akiko Takeda
2024	J	`jnl`	A Framework for Bilevel Optimization on Riemannian Manifolds. CoRR Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda
2024	J	`jnl`	Initial Placement for Fruchterman-Reingold Force Model with Coordinate Newton Direction. CoRR Hiroki Hamaguchi, Naoki Marumo, Akiko Takeda
2024	J	`jnl`	Parameter-Free Accelerated Gradient Descent for Nonconvex Minimization. SIAM J. Optim. Naoki Marumo, Akiko Takeda
2024	A*	`conf`	SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining. NeurIPS Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Kumar Jawanpuria, Bamdev Mishra
2024	J	`jnl`	SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining. CoRR Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Jawanpuria, Bamdev Mishra
2024	J	`jnl`	Stable Linear System Identification With Prior Knowledge by Riemannian Sequential Quadratic Optimization. IEEE Trans. Autom. Control. Mitsuaki Obara, Kazuhiro Sato, Hiroki Sakamoto, Takayuki Okuno, Akiko Takeda
2024	J	`jnl`	Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions. CoRR Yuya Hikima, Akiko Takeda
2023	J	`jnl`	A study on modularity density maximization: Column generation acceleration and computational complexity analysis. Eur. J. Oper. Res. Issey Sukeda, Atsushi Miyauchi, Akiko Takeda
2023	J	`jnl`	Complexity analysis of interior-point methods for second-order stationary points of nonlinear semidefinite optimization problems. Comput. Optim. Appl. Shun Arahata, Takayuki Okuno, Akiko Takeda
2023	J	`jnl`	Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints. Comput. Optim. Appl. Tianxiang Liu, Ting Kei Pong, Akiko Takeda
2023	J	`jnl`	Majorization-minimization-based Levenberg-Marquardt method for constrained nonlinear least squares. Comput. Optim. Appl. Naoki Marumo, Takayuki Okuno, Akiko Takeda
2023	A	`conf`	Robust Gaussian process regression with the trimmed marginal likelihood. UAI Daniel Andrade, Akiko Takeda
2022	J	`jnl`	A Study on Modularity Density Maximization: Column Generation Acceleration and Computational Complexity Analysis. CoRR Issey Sukeda, Atsushi Miyauchi, Akiko Takeda
2022	J	`jnl`	An inexact successive quadratic approximation method for a class of difference-of-convex optimization problems. Comput. Optim. Appl. Tianxiang Liu, Akiko Takeda
2022	J	`jnl`	Convexification with Bounded Gap for Randomly Projected Quadratic Optimization. SIAM J. Optim. Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda
2022	J	`jnl`	Perturbed Iterate SGD for Lipschitz Continuous Loss Functions. J. Optim. Theory Appl. Michael R. Metel, Akiko Takeda
2022	J	`jnl`	Sequential Quadratic Optimization for Nonlinear Optimization Problems on Riemannian Manifolds. SIAM J. Optim. Mitsuaki Obara, Takayuki Okuno, Akiko Takeda
2022	A*	`conf`	Single Loop Gaussian Homotopy Method for Non-convex Optimization. NeurIPS Hidenori Iwakiri, Yuhang Wang, Shinji Ito, Akiko Takeda
2021	A*	`conf`	A Gradient Method for Multilevel Optimization. NeurIPS Ryo Sato, Mirai Tanaka, Akiko Takeda
2021	J	`jnl`	A Gradient Method for Multilevel Optimization. CoRR Ryo Sato, Mirai Tanaka, Akiko Takeda
2021	A	`conf`	A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion. CIKM Naoki Marumo, Atsushi Miyauchi, Akiko Takeda, Akira Tanaka
2021	J	`jnl`	A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion. CoRR Naoki Marumo, Atsushi Miyauchi, Akiko Takeda, Akira Tanaka
2021	J	`jnl`	BODAME: Bilevel Optimization for Defense Against Model Extraction. CoRR Yuto Mori, Atsushi Nitanda, Akiko Takeda
2021	J	`jnl`	On lp-hyperparameter Learning via Bilevel Nonsmooth Optimization. J. Mach. Learn. Res. Takayuki Okuno, Akiko Takeda, Akihiro Kawana, Motokazu Watanabe
2021	J	`jnl`	Primal-dual subgradient method for constrained convex optimization problems. Optim. Lett. Michael R. Metel, Akiko Takeda
2021	J	`jnl`	Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization. J. Mach. Learn. Res. Michael R. Metel, Akiko Takeda
2020	J	`jnl`	A Hybrid Penalty Method for a Class of Optimization Problems with Multiple Rank Constraints. SIAM J. Matrix Anal. Appl. Tianxiang Liu, Ivan Markovsky, Ting Kei Pong, Akiko Takeda
2020	J	`jnl`	Construction Methods of the Nearest Positive System. IEEE Control. Syst. Lett. Kazuhiro Sato, Akiko Takeda
2020	J	`jnl`	Controllability Maximization of Large-Scale Systems Using Projected Gradient Method. IEEE Control. Syst. Lett. Kazuhiro Sato, Akiko Takeda
2020	—	`conf`	Convex Fairness Constrained Model Using Causal Effect Estimators. WWW (Companion Volume) Hikaru Ogura, Akiko Takeda
2020	J	`jnl`	Convex Fairness Constrained Model Using Causal Effect Estimators. CoRR Hikaru Ogura, Akiko Takeda
2020	J	`jnl`	Data-Driven Structured Noise Filtering via Common Dynamics Estimation. IEEE Trans. Signal Process. Ivan Markovsky, Tianxiang Liu, Akiko Takeda
2020	J	`jnl`	Estimation of Gaussian mixture models via tensor moments with application to online learning. Pattern Recognit. Lett. Donya Rahmani, Mahesan Niranjan, Damien Fay, Akiko Takeda, Jacek Brodzki
2020	J	`jnl`	Generalized Subdifferentials of Spectral Functions over Euclidean Jordan Algebras. SIAM J. Optim. Bruno F. Lourenço, Akiko Takeda
2020	J	`jnl`	Robust Bayesian model selection for variable clustering with the Gaussian graphical model. Stat. Comput. Daniel Andrade, Akiko Takeda, Kenji Fukumizu
2020	J	`jnl`	Theory and Algorithms for Shapelet-Based Multiple-Instance Learning. Neural Comput. Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda
2020	J	`jnl`	Theory and Algorithms for Shapelet-based Multiple-Instance Learning. CoRR Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda
2019	J	`jnl`	A refined convergence analysis of \(\hbox {pDCA}_{e}\) with applications to simultaneous sparse recovery and outlier detection. Comput. Optim. Appl. Tianxiang Liu, Ting Kei Pong, Akiko Takeda
2019	J	`jnl`	A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems. Math. Program. Tianxiang Liu, Ting Kei Pong, Akiko Takeda
2019	J	`jnl`	Algorithm 996: BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems With Binary, Box, and Complementarity Constraints. ACM Trans. Math. Softw. Naoki Ito, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Kim-Chuan Toh
2019	A*	`conf`	Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization. ICML Michael R. Metel, Akiko Takeda
2019	—	`conf`	Subspace methods for multi-channel sum-of-exponentials common dynamics estimation. CDC Ivan Markovsky, Tianxiang Liu, Akiko Takeda
2018	J	`jnl`	DC formulations and algorithms for sparse optimization problems. Math. Program. Jun-ya Gotoh, Akiko Takeda, Katsuya Tono
2018	J	`jnl`	Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems. J. Glob. Optim. Naoki Ito, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Kim-Chuan Toh
2018	J	`jnl`	Improving cash logistics in bank branches by coupling machine learning and robust optimization. Expert Syst. Appl. Jorge López Lázaro, Álvaro Barbero Jiménez, Akiko Takeda
2018	J	`jnl`	Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers. CoRR Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda
2018	A*	`conf`	Nonconvex Optimization for Regression with Fairness Constraints. ICML Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao
2018	A*	`conf`	Robust Densest Subgraph Discovery. ICDM Atsushi Miyauchi, Akiko Takeda
2018	J	`jnl`	Robust Densest Subgraph Discovery. CoRR Atsushi Miyauchi, Akiko Takeda
2018	J	`jnl`	Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization. J. Glob. Optim. Shinji Yamada, Akiko Takeda
2017	J	`jnl`	A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification. J. Mach. Learn. Res. Naoki Ito, Akiko Takeda, Kim-Chuan Toh
2017	J	`jnl`	Boosting the kernelized shapelets: Theory and algorithms for local features. CoRR Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda
2017	J	`jnl`	Breakdown Point of Robust Support Vector Machines. Entropy Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
2017	J	`jnl`	DC Algorithm for Extended Robust Support Vector Machine. Neural Comput. Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori
2017	J	`jnl`	Exact Semidefinite Programming Relaxations with Truncated Moment Matrix for Binary Polynomial Optimization Problems. SIAM J. Optim. Shinsaku Sakaue, Akiko Takeda, Sunyoung Kim, Naoki Ito
2017	—	`conf`	Household energy consumption prediction by feature selection of lifestyle data. SmartGridComm Kosuke Nishida, Akiko Takeda, Satoru Iwata, Mariko Kiho, Isao Nakayama
2017	J	`jnl`	Optimistic Robust Optimization With Applications To Machine Learning. CoRR Matthew Norton, Akiko Takeda, Alexander Mafusalov
2017	—	`conf`	Position-based Multiple-play Bandit Problem with Unknown Position Bias. NIPS Junpei Komiyama, Junya Honda, Akiko Takeda
2017	J	`jnl`	Robustness of learning algorithms using hinge loss with outlier indicators. Neural Networks Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
2017	J	`jnl`	Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem. SIAM J. Optim. Satoru Adachi, Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda
2017	—	`conf`	Trimmed Density Ratio Estimation. NIPS Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu
2016	J	`jnl`	Solving Generalized CDT Problems via Two-Parameter Eigenvalues. SIAM J. Optim. Shinsaku Sakaue, Yuji Nakatsukasa, Akiko Takeda, Satoru Iwata
2015	J	`jnl`	Computing the Signed Distance Between Overlapping Ellipsoids. SIAM J. Optim. Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda
2015	J	`jnl`	Cyber Security Analysis of Power Networks by Hypergraph Cut Algorithms. IEEE Trans. Smart Grid Yutaro Yamaguchi, Anna Ogawa, Akiko Takeda, Satoru Iwata
2015	J	`jnl`	Geometric intuition and algorithms for Ev-SVM. J. Mach. Learn. Res. Álvaro Barbero Jiménez, Akiko Takeda, Jorge López Lázaro
2015	J	`jnl`	Optimizing over coherent risk measures and non-convexities: a robust mixed integer optimization approach. Comput. Optim. Appl. Dimitris Bertsimas, Akiko Takeda
2015	J	`jnl`	Outlier detection at the transcriptome-proteome interface. Bioinform. Yawwani Gunawardana, Shuhei Fujiwara, Akiko Takeda, Jeongmin Woo, Christopher H. Woelk, Mahesan Niranjan
2015	A	`conf`	Robust Cost Sensitive Support Vector Machine. AISTATS Shuichi Katsumata, Akiko Takeda
2014	J	`jnl`	Breakdown Point of Robust Support Vector Machine. CoRR Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
2014	—	`conf`	Cyber security analysis of power networks by hypergraph cut algorithms. SmartGridComm Yutaro Yamaguchi, Anna Ogawa, Akiko Takeda, Satoru Iwata
2014	—	`conf`	Exact SVM training by Wolfe's minimum norm point algorithm. MLSP Masashi Kitamura, Akiko Takeda, Satoru Iwata
2014	J	`jnl`	Extended Robust Support Vector Machine Based on Financial Risk Minimization. Neural Comput. Akiko Takeda, Shuhei Fujiwara, Takafumi Kanamori
2014	A	`conf`	Global Optimization Methods for Extended Fisher Discriminant Analysis. AISTATS Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda
2014	J	`jnl`	Interaction between financial risk measures and machine learning methods. Comput. Manag. Sci. Jun-ya Gotoh, Akiko Takeda, Rei Yamamoto
2014	C	`conf`	Memory-efficient large-scale linear support vector machine. ICMV Abdullah Alrajeh, Akiko Takeda, Mahesan Niranjan
2014	J	`jnl`	Numerical study of learning algorithms on Stiefel manifold. Comput. Manag. Sci. Takafumi Kanamori, Akiko Takeda
2014	J	`jnl`	Using financial risk measures for analyzing generalization performance of machine learning models. Neural Networks Akiko Takeda, Takafumi Kanamori
2013	J	`jnl`	A Unified Classification Model Based on Robust Optimization. Neural Comput. Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
2013	J	`jnl`	Conjugate relation between loss functions and uncertainty sets in classification problems. J. Mach. Learn. Res. Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
2013	—	`conf`	Convex hull pricing for demand response in electricity markets. SmartGridComm Naoki Ito, Akiko Takeda, Toru Namerikawa
2013	—	`conf`	Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. NIPS Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
2013	J	`jnl`	Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios. Comput. Manag. Sci. Akiko Takeda, Mahesan Niranjan, Jun-ya Gotoh, Yoshinobu Kawahara
2012	J	`jnl`	A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems CoRR Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
2012	A*	`conf`	A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. COLT Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
2012	J	`jnl`	A Unified Robust Classification Model CoRR Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
2012	A*	`conf`	A Unified Robust Classification Model. ICML Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
2012	J	`jnl`	Minimizing loss probability bounds for portfolio selection. Eur. J. Oper. Res. Jun-ya Gotoh, Akiko Takeda
2012	—	`conf`	Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning. ICONIP (1) Takafumi Kanamori, Akiko Takeda
2012	J	`jnl`	Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty. J. Optim. Theory Appl. Takafumi Kanamori, Akiko Takeda
2011	J	`jnl`	On the role of norm constraints in portfolio selection. Comput. Manag. Sci. Jun-ya Gotoh, Akiko Takeda
2010	J	`jnl`	A relaxation algorithm with a probabilistic guarantee for robust deviation optimization. Comput. Optim. Appl. Akiko Takeda, Shunsuke Taguchi, Tsutomu Tanaka
2009	J	`jnl`	A robust approach based on conditional value-at-risk measure to statistical learning problems. Eur. J. Oper. Res. Akiko Takeda, Takafumi Kanamori
2009	J	`jnl`	Generalization performance of nu-support vector classifier based on conditional value-at-risk minimization. Neurocomputing Akiko Takeda
2009	J	`jnl`	On Generalization Performance and Non-Convex Optimization of Extended New Gener. Comput. Akiko Takeda, Masashi Sugiyama
2008	J	`jnl`	Conditional minimum volume ellipsoid with application to multiclass discrimination. Comput. Optim. Appl. Jun-ya Gotoh, Akiko Takeda
2007	J	`jnl`	Dynamic Enumeration of All Mixed Cells. Discret. Comput. Geom. Tomohiko Mizutani, Akiko Takeda, Masakazu Kojima
2004	—	`conf`	High Performance Grid and Cluster Computing for Some Optimization Problems. SAINT Workshops Katsuki Fujisawa, Masakazu Kojima, Akiko Takeda, Makoto Yamashita
2004	J	`jnl`	PHoM - a Polyhedral Homotopy Continuation Method for Polynomial Systems. Computing Takayuki Gunji, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Katsuki Fujisawa, Tomohiko Mizutani
2002	J	`jnl`	Parallel Implementation of Successive Convex Relaxation Methods for Quadratic Optimization Problems. J. Glob. Optim. Akiko Takeda, Katsuki Fujisawa, Yusuke Fukaya, Masakazu Kojima
2001	J	`jnl`	Complexity Analysis of Successive Convex Relaxation Methods for Nonconvex Sets. Math. Oper. Res. Masakazu Kojima, Akiko Takeda
2001	J	`jnl`	On measuring the inefficiency with the inner-product norm in data envelopment analysis. Eur. J. Oper. Res. Akiko Takeda, Hisakazu Nishino