Research Activities (Papers)

Refereed Journals

  1. M. Obara, K. Sato, H. Sakamoto, T. Okuno, A. Takeda,
    "Stable Linear System Identification with Prior Knowledge by Riemannian Sequential Quadratic Optimization”, accepted by The IEEE Transactions on Automatic Control, 2023.
  2. T. Liu, T.K. Pong, A. Takeda,
    "Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints", Computational Optimization and Applications, 86, pp. 521-–553 (2023). DOI: https://doi.org/10.1007/s10589-023-00503-1
  3. S. Arahata, T. Okuno, A. Takeda,
    " Complexity analysis of interior-point methods for second-order stationary points of nonlinear semidefinite optimization problems”, Computational Optimization and Applications, 86, pp. 555–-598 (2023). DOI: https://doi.org/10.1007/s10589-023-00501-3
  4. I. Sukeda, A. Miyauchi, A. Takeda,
    "A Study on Modularity Density Maximization: Column Generation Acceleration and Computational Complexity Analysis”, European Journal of Operational Research, 309 (2), pp. 516--528 (2023). DOI: https://doi.org/10.1016/j.ejor.2023.01.061
  5. P.L. Poirion, B.F. Lourenco, A. Takeda,
    "Random projection of Linear and Semidefinite problem with linear inequalities”, Linear Algebra and Its Applications, 664, pp. 24-60 (2023). DOI: https://doi.org/10.1016/j.laa.2023.01.013
  6. N. Marumo, T. Okuno, A. Takeda,
    "Majorization-minimization-based Levenberg–Marquardt method for constrained nonlinear least squares”, Computational Optimization and Applications, 84, pp. 833-874 (2023). DOI: https://doi.org/10.1007/s10589-022-00447-y [code]
  7. M. Metel, A. Takeda,
    "Perturbed Iterate SGD for Lipschitz Continuous Loss Functions", Journal of Optimization Theory and Applications, 195, pp.504--547 (2022). DOI: https://doi.org/10.1007/s10957-022-02093-0
  8. T. Liu and A. Takeda,
    "An inexact successive quadratic approximation method for a class of difference-of-convex optimization problems”, Computational Optimization and Applications, 82, pp.141-173 (2022). DOI: https://doi.org/10.1007/s10589-022-00357-z [code]
  9. M. Obara, T. Okuno, A. Takeda,
    " Sequential Quadratic Optimization for Nonlinear Optimization Problems on Riemannian Manifolds", SIAM Journal on Optimization, 32(2), pp.822-853 (2022). DOI: https://doi.org/10.1137/20M1370173 [code]
  10. T. Fuji, P.L. Poirion, A. Takeda,
    " Convexification with bounded gap for randomly projected quadratic optimization", SIAM Journal on Optimization, 32(2), pp.874-899 (2022). DOI: https://doi.org/10.1137/21M1433678
  11. T. Okuno, A. Takeda, A. Kawana, M. Watanabe,
    " Hyperparameter Learning via Bilevel Nonsmooth Optimization ", Journal of Machine Learning Research, 22(245), pp.1-47 (2021).
  12. M. Metel, A. Takeda,
    "Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization", Journal of Machine Learning Research, 22(115), pp.1-36 (2021).
  13. M. Metel, A. Takeda,
    "Primal-dual subgradient method for constrained convex optimization problems ", Optimization Letters, 15, pp.1491–1504 (2021). DOI: https://doi.org/10.1007/s11590-021-01728-x
  14. B.F. Lourenco, A. Takeda,
    "Generalized subdifferentials of spectral functions over Euclidean Jordan algebras ", SIAM Journal on Optimization, 30(4), pp.3387-3414 (2020). DOI: https://doi.org/10.1137/19M1245001
  15. T. Liu, I. Markovsky, T.K. Pong, A. Takeda,
    "A hybrid penalty method for a class of optimization problems with multiple rank constraints",
    SIAM Journal on Matrix Analysis and Applications, 41(3), pp.1260-1283 (2020). DOI: https://doi.org/10.1137/19M1269919
  16. I. Markovsky, T. Liu, A. Takeda,
    "Data-driven structured noise filtering via common dynamics estimation",
    IEEE Transactions on Signal Processing, 68, pp.3064-3073 (2020). DOI: 10.1109/TSP.2020.2993676
  17. K. Sato, A. Takeda,
    ""Controllability maximization of large-scale systems using projected gradient method",
    IEEE Control Systems Letters (L-CSS), 4(4), pp.821-826 (2020). DOI: 10.1109/LCSYS.2020.2993983
  18. D. Suehiro, K. Hatano, E. Takimoto, S. Yamamoto, K. Bannai, A. Takeda,
    "Theory and Algorithms for Shapelet-based Multiple-Instance Learning",
    Neural Computation, 32 (8), pp.1580-1613 (2020). DOI: https://doi.org/10.1162/neco_a_01297
  19. D. Rahmani, M. Niranjan, D. Fay, A. Takeda, J. Brodzki,
    " Estimation of Gaussian Mixture Models via Tensor Moments with Application to Online Learning",
    Pattern Recognition Letters, 131, pp.285-292 (2020). DOI: https://doi.org/10.1016/j.patrec.2020.01.001
  20. D. Andrade, A. Takeda, K. Fukumizu,
    "Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model",
    Statistics and Computing, 30, pp.351–376 (2020). DOI: https://doi.org/10.1007/s11222-019-09879-9
  21. K. Sato, A. Takeda,
    "Construction methods of the nearest positive system",
    the IEEE Control Systems Society Letters (LCSS), 4(1), pp.97-102 (2020). DOI: 10.1109/LCSYS.2019.2921838
  22. N. Ito, S. Kim, M. Kojima, A. Takeda, K.C. Toh,
    "BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems with Binary, Box and Complementarity Constraints",
    ACM Transactions on Mathematical Software, 45(3) (2019). DOI: https://doi.org/10.1145/3309988
  23. T. Liu, T.K. Pong, A. Takeda,
    "A refined convergence analysis of pDCAe with applications to simultaneous sparse recovery and outlier detection",
    Computational Optimization and Applications, 73(1), pp.69-100 (2019). DOI: https://doi.org/10.1007/s10589-019-00067-z
  24. T. Liu, T.K. Pong, A. Takeda,
    "A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems",
    Mathematical Programming, 176, pp.339–367 (2019). DOI: https://doi.org/10.1007/s10107-018-1327-8
  25. N. Ito, S. Kim, M. Kojima, A. Takeda and K.C. Toh,
    "Equivalences and Differences in Conic Relaxations of Combinatorial Quadratic Optimization Problems",
    Journal of Global Optimization, 72(4), pp.619-653 (2018). DOI: https://doi.org/10.1007/s10898-018-0676-4
  26. S. Yamada, A. Takeda,
    "Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization",
    Journal of Global Optimization, 71(2), pp.313-339 (2018). DOI: 10.1007/s10898-018-0617-2
  27. R. Konishi, A. Takeda, M. Takahashi,
    " Optimal Sizing of Energy Storage Systems for the Energy Procurement Problem in Multi-period Markets under Uncertainties",
    Energies, 11(1), 158 (2018). DOI:10.3390/en11010158
  28. J. Lopez, A. Barbero and A. Takeda,
    "Improving Cash Management in Bank Branches by combining Machine Learning and Robust Optimization",
    Expert Systems With Applications, 92, pp.236-255 (2018). https://doi.org/10.1016/j.eswa.2017.09.043
  29. J. Gotoh, A. Takeda and K. Tono,
    "DC Formulations and Algorithms for Sparse Optimization Problems",
    Mathematical Programming, 169 (1), pp.141-176 (2018). DOI: 10.1007/s10107-017-1181-0
  30. T. Kanamori, S. Fujiwara and A. Takeda,
    "Robustness of Learning Algorithms using Hinge Loss with Outlier Indicators",
    Neural Networks, 94, pp.173-191 (2017). DOI: 10.1016/j.neunet.2017.07.005
  31. T. Kanamori, S. Fujiwara, A. Takeda,
    " Breakdown Point of Robust Support Vector Machine", Entropy 2017, 19, 83 (2017). DOI:10.3390/e19020083
  32. S. Fujiwara, A. Takeda, T. Kanamori,
    "DC Algorithm for Extended Robust Support Vector Machine", Neural Computation, 29(5), pp.1406-1438 (2017). DOI: 10.1162/NECO_a_00958
  33. N. Ito, A. Takeda and K.C. Toh,
    "A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification", Journal of Machine Learning Research, 18, pp.1-49 (2017).
  34. S. Sakaue, A. Takeda, S. Kim and N. Ito,
    "Exact SDP Relaxations with Truncated Moment Matrix for Binary Polynomial Optimization Problems", SIAM Journal on Optimization, 27 (1), pp. 565-582 (2017). DOI: 10.1137/16M105544X
  35. S. Adachi, S. Iwata, Y. Nakatsukasa and A. Takeda,
    "Solving the Trust Region Subproblem by a Generalized Eigenvalue Problem", SIAM Journal on Optimization, 27 (1), pp.269-291 (2017). DOI: 10.1137/16M1058200
  36. S. Sakaue, Y. Nakatsukasa, A. Takeda and S. Iwata,
    "Solving generalized CDT problems via two-parameter eigenvalues", SIAM Journal on Optimization, 26 (3), pp.1669-1694 (2016). DOI:10.1137/15100624X
  37. S. Iwata, Y. Nakatsukasa and A. Takeda,
    "Computing the signed distance between overlapping ellipsoids", SIAM Journal on Optimization, 25 (4), pp.2359-2384 (2015). DOI: :10.1137/140979654
  38. D. Bertsimas and A. Takeda,
    "Optimizing Over Coherent Risk Measures and Non-convexities: A Robust Mixed Integer Optimization Approach",
    Computational Optimization and Applications 62 (3), pp.613-639 (2015). DOI: 10.1007/s10589-015-9755-3
  39. Y. Gunawardana, S. Fujiwara, A. Takeda, J. Woo, C. Woelk, M. Niranjan,
    "Outlier-Detection at the Transcriptome-Proteome Interface",
    Bioinformatics, 31 (15), pp.2530-2536 (2015). DOI: 10.1093/bioinformatics/btv182
  40. Y. Yamaguchi, A. Ogawa, A. Takeda and S. Iwata,
    "Cyber Security Analysis of Power Networks by Hypergraph Cut Algorithms",
    The IEEE Transactions on Smart Grid, 6 (5), pp.2189-2199 (2015). DOI: 10.1109/TSG.2015.2394791
  41. A. Barbero, A. Takeda and J. Lopez,
    "Geometric intuition and algorithms for Enu-SVM",
    Journal of Machine Learning Research, 16, pp.323-369 (2015).
  42. A. Takeda and T. Kanamori,
    "Using Financial Risk Measures for Analyzing Generalization Performance of Machine Learning Models",
    Neural Networks , 57, pp.29-38 (2014). DOI: 10.1016/j.neunet.2014.05.006
  43. A. Takeda, S. Fujiwara and T. Kanamori,
    "Extended Robust Support Vector Machine Based on Financial Risk Minimization",
    Neural Computation, 26 (11), pp.2541-2569 (2014). DOI: 10.1162/NECO_a_00647
  44. T. Kanamori and A. Takeda,
    "Numerical Study of Learning Algorithms on Stiefel Manifold",
    Computational Management Science, 11 (4), pp.319-340 (2014). DOI: 10.1007/s10287-013-0181-7
  45. J. Gotoh, A. Takeda and R. Yamamoto,
    "Interactions between Financial Risk Measures and Machine Learning Methods",
    Computational Management Science, 11 (4), pp.365-402 (2014). DOI: 10.1007/s10287-013-0175-5
  46. T. Kanamori, A. Takeda and T. Suzuki,
    "A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems",
    Journal of Machine Learning Research, 14, pp.1461−1504 (2013).
  47. S. Okido and A. Takeda,
    " Economic and Environmental Analysis of Photovoltaic Energy Systems via Robust Optimization",
    Energy Systems, 4, pp.239-266 (2013). DOI: 10.1007/s12667-013-0077-1
  48. J. Gotoh, K. Shinozaki and A. Takeda,
    "Robust Portfolio Techniques for Mitigating the Fragility of CVaR Minimization and Generalization to Coherent Risk Measures",
    Quantitative Finance, 13 (10), pp.1621-1635 (2013). DOI: 10.1080/14697688.2012.738930
  49. A. Takeda, H. Mitsugi and T. Kanamori,
    "A Unified Classification Model Based on Robust Optimization",
    Neural Computation, 25 (3), pp.759-804 (2013). DOI: 10.1162/NECO_a_00412
  50. A. Takeda, M. Niranjan, J. Gotoh and Y. Kawahara,
    "Simultaneous Pursuit of Out-of-Sample Performance and Sparsity in Index Tracking Portfolios",
    Computational Management Science, 10 (1), pp.21-49 (2013). DOI: 10.1007/s10287-012-0158-y
  51. J. Gotoh and A. Takeda,
    "Minimizing Loss Probability Bounds for Portfolio Selection",
    European Journal of Operational Research, 217 (2), pp.371-380 (2012). DOI: 10.1016/j.ejor.2011.09.012
  52. T. Kanamori and A. Takeda,
    "Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty",
    Journal of Optimization Theory and Applications, 152 (1), pp.171-197 (2012). DOI: 10.1007/s10957-011-9923-2
  53. J. Gotoh and A. Takeda,
    "On the Role of Norm Constraints in Portfolio Selection",
    Computational Management Science, 8 (4), pp.323-353 (2011). DOI: 10.1007/s10287-011-0130-2
  54. A. Takeda, S. Taguchi and T. Tanaka,
    "A Relaxation Algorithm with a Probabilistic Guarantee for Robust Deviation Optimization",
    Computational Optimization and Applications, 47 (1), pp.1-31 (2010). DOI: 10.1007/s10589-008-9212-7
  55. A. Takeda and M. Sugiyama,
    "On generalization performance and non-convex optimization of extended nu-support vector machine",
    New Generation Computing, 27, pp.259-279 (2009). DOI: 10.1007/s00354-008-0064-6
  56. A. Takeda,
    "Generalization Performance of nu-Support Vector Classifier Based on Conditional Value-at-Risk Minimization",
    Neurocomputing, 72 (10-12), pp.2351-2358 (2009). DOI: 10.1016/j.neucom.2008.11.022
  57. A. Takeda and T. Kanamori,
    "A Robust Approach Based on Conditional Value-at-Risk Measure to Statistical Learning Problems",
    European Journal of Operational Research, 198 (1), pp. 287-296 (2009). DOI: 10.1016/j.ejor.2008.07.027
  58. J. Gotoh and A. Takeda,
    "Conditional Minimum Volume Ellipsoid with Applications to Multiclass Discrimination",
    Computational Optimization and Applications, 41 (1), pp.27-51 (2008). DOI: 10.1007/s10589-007-9097-x
  59. A. Takeda, S. Taguchi and R. Tutuncu,
    "Adjustable Robust Optimization Models for a Nonlinear Two-Period System",
    Journal of Optimization Theory and Applications, 136 (2), pp.275-295 (2008). DOI: 10.1007/s10957-007-9288-8
  60. T. Mizutani, A. Takeda and M. Kojima,
    "Dynamic Enumeration of All Mixed Cells",
    Discrete and Computational Geometry, 37 (3), pp.351-367 (2007). DOI: 10.1007/s00454-006-1300-9
  61. A. Takeda, N. Uchihira, M. Nakamoto and S. Matsumoto,
    "An Electric Powerplant Planning Method for Uncertain Environments" (Japanese),
    Journal of Japan Industrial Management Association, 56 (5), pp.366-376 (2005).
  62. J. Gotoh and A. Takeda,
    "A linear Classification Model Based on Conditional Geometric Score",
    Pacific Journal of Optimization, 1 (2),pp.277-296 (2005).
  63. K. Fujisawa, M. Kojima, A. Takeda and M. Yamashita,
    "Solving Large Scale Optimization Problems via Grid and Cluster Computing".
    Journal of the Operations Research Society of Japan, 47(4),pp.265-274 (2004).
  64. T. Gunji, S. Kim, M. Kojima, A. Takeda, K. Fujisawa and T. Mizutani,
    "PHoM -- a Polyhedral Homotopy Continuation Method".
    Computing, 73, pp.57-77 (2004). DOI: 10.1007/s00607-003-0032-4
  65. C. Vo, A. Takeda and M. Kojima,
    "A Multilevel Parallelized Hybrid Branch and Bound Algorithm for Quadratic Optimization",
    IPSJ Transactions on Advanced Computing Systems, 45 SIG 6(ACS 6), pp.186-196 (2004).
  66. A. Takeda, K. Fujisawa, Y. Fukaya and M. Kojima,
    "Parallel Implementation of Successive Convex Relaxation Methods for Quadratic Optimization Problems",
    Journal of Global Optimization, 24 (2), pp.237-260 (2002).
  67. A. Takeda, M. Kojima and K. Fujisawa,
    "Enumeration of All Solutions of a Combinatorial linear Inequality System Arising from
    the Polyhedral Homotopy Continuation Method",
    Journal of the Operations Research Society of Japan, 45 (1), pp.64-82 (2002).
  68. A. Takeda and H. Nishino,
    "On Measuring the Inefficiency with the Inner-Product Norm in Date Envelopment Analysis",
    European Journal of Operational Research, Vol.133 (2), pp.377-393 (2001).
  69. M. Kojima and A. Takeda,
    "Complexity Analysis of Successive Convex Relaxation Methods for Nonconvex Sets",
    Mathematics of Operations Research, 26 (3), pp.519-542 (2001).

Refereed Book Chapters

  1. T. Mizutani and A. Takeda,
    "DEMiCs: A software package for computing the mixed volume via dynamic enumeration of all mixed cells",
    in M.E. Stillman, N. Takayama and J. Verschelde (Eds.),
    IMA Volumes on "Software for algebraic geometry", pp.59-79 (2008).
  2. A. Takeda and M. Kojima,
    "Successive Convex Relaxation Approach to Bilevel Quadratic Optimization Problems",
    in M. C. Ferris, O. L. Mangasarian and J.S. Pang (Eds.),
    Applications and Algorithms of Complementarity, Kluwer Academic Publishers, p.317-p.340 (2001).
  3. A. Takeda, Y. Dai, M. Fukuda, and M. Kojima,
    "Towards the Implementation of Successive Convex Relaxation Method for Nonconvex Quadratic Optimization Problems",
    in P.M. Pardalos (Ed.), Approximation and Complexity in Numerical Optimization:
    Continuous and Discrete Problems    , Kluwer Academic Publishers, p.489-p.510 (2000).

Refereed Conference Proceedings

  1. D. Andrade, A. Takeda, "Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model", the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023), 2023.
  2. H. Iwakiri, Y. Wang, S. Ito, A. Takeda,
    "Single Loop Gaussian Homotopy Method for Non-convex Optimization", Neural Information Processing Systems 35 (Neurips 2022), 2022.
  3. R. Sato, M. Tanaka, A. Takeda,
    "A Gradient Method for Multilevel Optimization”,
    Neural Information Processing Systems 34 (Neurips 2021), pp. 7522-7533, 2021. [code]
  4. N. Marumo, A. Miyauchi, A. Takeda, A. Tanaka,
    "A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion",
    CIKM ’21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, October 2021, Pages 1274-1283, 2021.
  5. H. Ogura, A. Takeda,
    "Convex Fairness Constrained Model Using Causal Effect Estimators",
    FATES on the Web 2020, 2020.
  6. I. Markovsky, T. Liu, A. Takeda,
    "Subspace methods for multi-channel sum-of-exponentials common dynamics estimation",
    the 2019 IEEE Conference on Decision and Control (CDC), 2019.
  7. K. Sato, A. Takeda,
    "Construction methods of the nearest positive system",
    the 2019 IEEE Conference on Decision and Control (CDC), 2019.
  8. M. Metel, A. Takeda,
    "Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization",
    Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML 2019), 2019.
  9. A. Miyauchi, A. Takeda,
    "Robust Densest Subgraph Discovery",
    the 2018 IEEE International Conference on Data Mining (ICDM'18), 2018. [code]
  10. J. Komiyama, A. Takeda, J. Honda, H. Shimao,
    "Nonconvex Optimization for Regression with Fairness Constraints",
    35th International Conference on Machine Learning (ICML2018), 2018.
  11. S. Ikeda, A. Takeda, H. Ohmori,
    "Optimal Sizing of Photovoltaic Systems for Loss Minimization in Distribution Network",
    2018 SICE International Symposium on Control Systems, 2018.
  12. D. Suehiro, K. Hatano, E. Takimoto, S. Yamamoto, K. Bannai and A. Takeda,
    "Learning theory and algorithms for shapelets and other local features",
    NIPS 2017 Time Series Workshop, 2017.
  13. S. Liu, A. Takeda, T. Suzuki and K. Fukumizu,
    "Trimmed Density Ratio Estimation",
    the Thirty-First Annual Conference on Neural Information Processing Systems (NIPS 2017), 2017.
  14. J. Komiyama, J. Honda and A. Takeda,
    "Position-based Multiple-play Multi-armed Bandit Problem with Unknown Position Bias",
    the Thirty-First Annual Conference on Neural Information Processing Systems (NIPS 2017), 2017.
  15. K. Nishida, A. Takeda, S. Iwata, M. Kiho and I. Nakayama,
    "Household energy consumption prediction by feature selection of lifestyle data",
    IEEE International Conference on Smart Grid Communications, 2017.
  16. S. Katsumata and A. Takeda,
    " Robust Cost Sensitive Support Vector Machine",
    Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 2015. JMLR: W&CP volume 38, 2015.
  17. A. Alrajeh, A. Takeda, M. Niranjan,
    "Memory-Efficient Large-Scale Linear Support Vector Machine",
    The 7th International Conference on Machine Vision (ICMV 2014), 2014.
  18. Y. Gunawardana, S. Fujiwara, A. Takeda, C. Woelk and M. Niranjan,
    "Outlier-Detecting Support Vector Regression for Modelling at the Transcriptome-Proteome Interface",
    Eighth International Workshop on Machine Learning in Systems Biology (MLSB 2014), 2014.
  19. Y. Yamaguchi, A. Ogawa, A. Takeda, S. Iwata,
    "Cyber Security Analysis of Power Networks by Hypergraph Cut Algorithms",
    IEEE SmartGridComm 2014 Symposium, 2014.
  20. M. Kitamura, A. Takeda, S. Iwata,
    "Exact SVM Training by Wolfe's Minimum Norm Point Algorithm",
    Proceedings of 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2014), 2014.
  21. S. Iwata, Y. Nakatsukasa, A. Takeda,
    "Global Optimization Methods for Extended Fisher Discriminant Analysis",
    Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2014.
  22. A. Ogawa, A. Takeda and T. Namerikawa,
    "Photovoltaic Output Prediction Using Auto-regression with Support Vector Machine",
    NIPS 2013 workshop on Machine Learning for Sustainability, 2013.
  23. S. Nakajima, A. Takeda, S. D. Babacan, M. Sugiyama and I. Takeuchi,
    "Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering",
    The Neural Information Processing Systems (NIPS2013), 2013.
  24. N. Ito, A. Takeda and T. Namerikawa,
    "Convex Hull Pricing for Demand Response in Electricity Markets",
    IEEE SmartGridComm 2013 Symposium, 2013. DOI: 10.1109/SmartGridComm.2013.6687949
  25. T. Kanamori and A. Takeda,
    "Non-Convex Optimization on Stiefel Manifold and Applications to Machine Learning",
    The International Conference on Neural Information Processing (ICONIP2012), 2012.
  26. A. Takeda, H. Mitsugi and T. Kanamori,
    "A unified robust classification model",
    29th International Conference on Machine Learning (ICML2012), 2012.
  27. T. Kanamori, A. Takeda and T. Suzuki,
    "A conjugate property between loss functions and uncertainty sets in classification problems",
    Conference on Learning Theory (COLT2012), 2012.
  28. A. Takeda, J. Gotoh and M. Sugiyama, ``Support Vector Regression as Conditional Value-at-Risk Minimization with Application to Financial Time-series Analysis'', Proceedings of 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), Kittila, Finland, 2010.
  29. A. Takeda and M. Sugiyama, "Nu-Support Vector Machine as Conditional Value-at-Risk Minimization", Proceedings of the 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland, 2008. [paper]
  30. A. Takeda,
    "A Modified Algorithm for Nonconvex Support Vector Classification",
    Proceedings of the International Conference on Artificial Intelligence and Applications (AIA 2008), Innsbruck, Austria, 2008.
  31. K. Fujisawa, M. Kojima, A. Takeda and M. Yamashita,
    "High Performance Grid and Cluster Computing for Some Optimization Problems",
    2004 Symposium on Applications and the Internet (SAINT 2004 Workshops), pp.612-615 (2004).

Submitted Articles

  1. H. Iwakiri, T. Kamijima, S. Ito, A. Takeda,
    "Prediction-Correction Algorithm for Time-Varying Smooth Non-Convex Optimization", 2024.
  2. J.H. Alcantara, A. Takeda,
    "Theoretical smoothing frameworks for general nonsmooth bilevel problem", 2024.
  3. R. Nozawa, P.L. Poirion, A. Takeda,
    "Zeroth-order Random Subspace Algorithm for Non-smooth Convex Optimization", 2024.
  4. S. Takahashi, A. Takeda,
    "Approximate Bregman Proximal Gradient Algorithm for Relatively Smooth Nonconvex Optimization", 2023.
  5. V.T. Hieu and A. Takeda,
    "Computing local minimizers in polynomial optimization under genericity conditions", 2023.
  6. R. Nozawa, P.L. Poirion, and A. Takeda,
    "Randomized subspace gradient method for constrained optimization”, 2023.
  7. Y. Hikima, A. Takeda,
    "Stochastic Approach for Price Optimization Problems with Decision-dependent Uncertatainty”, 2023.
  8. N. Marumo, A. Takeda,
    "Universal heavy-ball method for nonconvex optimization under Holder continuous Hessians”, 2023.
  9. N. Marumo, A. Takeda,
    "Parameter-free accelerated gradient descent for nonconvex minimization”, 2022.
  10. S. Adachi, T. Okuno, A. Takeda,
    "Riemannian Levenberg-Marquardt Method with Global and Local Convergence Properties", 2022.
  11. T. Fuji, P.L. Poirion, A. Takeda,
    "Randomized Subspace Regularized Newton Method for Unconstrained Non-convex Optimization", 2022.
  12. N. Marumo, T. Okuno, A. Takeda,
    "Accelerated-gradient-based generalized Levenberg--Marquardt method with oracle complexity bound and local quadratic convergence", 2022.
  13. K. Sato, A. Takeda, T. Suzuki, R. Kawai,
    "Convergence error analysis of RGLD Convergence error analysis of reflected gradient Langevin dynamics for globally optimizing non-convex constrained problems ", 2022.
  14. J.H. Alcantara, C.T. Nguyen, T. Okuno, A. Takeda, J.S. Chen,
    "Unified Smoothing Approach for Best Hyperparameter Selection Problem Using a Bilevel Optimization Strategy”, 2021.

Doctor Thesis

  1. "Successive Convex Relaxation Methods for Nonconvex Quadratic Optimization Problems"
    (PDF file, PS file), Doctor Thesis, March 2001.
    Abstract