研究業績

雑誌に掲載された査読付き論文

  1. J. Lopez, A. Barbero and A. Takeda, "Improving Cash Management in Bank Branches by combining Machine Learning and Robust Optimization", accepted by Expert Systems With Applications, 2017.
  2. J. Gotoh, A. Takeda and K. Tono,
    "DC Formulations and Algorithms for Sparse Optimization Problems", accepted by Mathematical Programming, 2017. DOI: 10.1007/s10107-017-1181-0
  3. T. Kanamori, S. Fujiwara and A. Takeda, "Robustness of Learning Algorithms using Hinge Loss with Outlier Indicators", accepted by Neural Networks, 2017.
  4. T. Kanamori, S. Fujiwara, A. Takeda,
    " Breakdown Point of Robust Support Vector Machine", Entropy 2017, 19, 83 (2017). DOI:10.3390/e19020083
  5. 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
  6. 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).
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. A. Barbero, A. Takeda and J. Lopez,
    "Geometric intuition and algorithms for Enu-SVM",
    Journal of Machine Learning Research, 16, pp.323-369 (2015).
  15. 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
  16. 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
  17. 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
  18. J. Goto, 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
  19. 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).
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. J. Gotoh and A. Takeda,
    "Conditional Minimum Volume Ellipsoid with Application to Multiclass Discrimination",
    Computational Optimization and Applications, 41 (1), pp.27-51 (2008). DOI: 10.1007/s10589-007-9097-x
  32. 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
  33. 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
  34. 武田,内平,中本,松本,
    "不確実な電力事業環境下における発電設備投資計画法",
    日本経営工学会論文誌, 56 (5), pp.366-376 (2005).
  35. J. Gotoh and A. Takeda,
    "A linear Classification Model Based on Conditional Geometric Score",
    Pacific Journal of Optimization, 1 (2),pp.277-296 (2005).
  36. 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).
  37. 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
  38. 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).
  39. 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).
  40. 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).
  41. 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).
  42. 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).

書籍に掲載された査読付き論文

  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). DOI: 10.1007/978-0-387-78133-4_5
  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).

国際会議論文 (査読付き)

  1. S. Liu, A. Takeda, T. Suzuki and K. Fukumizu,
    "Trimmed Density Ratio Estimation",
    accepted by the Thirty-First Annual Conference on Neural Information Processing Systems (NIPS 2017), 2017.
  2. J. Komiyama, J. Honda and A. Takeda,
    "Position-based Multiple-play Multi-armed Bandit Problem with Unknown Position Bias",
    accepted by the Thirty-First Annual Conference on Neural Information Processing Systems (NIPS 2017), 2017.
  3. K. Nishida, A. Takeda, S. Iwata, M. Kiho and I. Nakayama,
    "Household energy consumption prediction by feature selection of lifestyle data",
    accepted by IEEE International Conference on Smart Grid Communications, 2017.
  4. 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.
  5. A. Alrajeh, A. Takeda, M. Niranjan,
    "Memory-Efficient Large-Scale Linear Support Vector Machine",
    The 7th International Conference on Machine Vision (ICMV 2014), 2014.
  6. 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.
  7. Y. Yamaguchi, A. Ogawa, A. Takeda, S. Iwata,
    "Cyber Security Analysis of Power Networks by Hypergraph Cut Algorithms",
    IEEE SmartGridComm 2014 Symposium, 2014.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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
  13. 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.
  14. A. Takeda, H. Mitsugi and T. Kanamori,
    "A unified robust classification model",
    29th International Conference on Machine Learning (ICML2012), 2012.
  15. 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.
  16. 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. DOI: 201010.1109/MLSP.2010.5589245
  17. 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]
  18. 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.
  19. 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).

投稿中の論文

  1. K. Tono, A. Takeda and J. Gotoh,
    "Efficient DC Algorithm for Constrained Sparse Optimization", 2017.
  2. S. Yamada, A. Takeda,
    "Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization", 2017.
  3. N. Ito, S. Kim, M. Kojima, A. Takeda and K.C. Toh,
    "Equivalences and Differences in Conic Relaxations of Combinatorial Quadratic Optimization Problems", 2017.
  4. T. Liu, T.K. Pong, A. Takeda,
    "A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems", 2017.
  5. M. Norton, A. Takeda, A. Mafusalov,
    "Optimistic Robust Optimization with Applications to Linear Programming and Machine Learning", 2017.

Doctor Thesis

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