Presentation


  ‘Û”­•\:

  1. Mariko Yamamura, Mineaki Ohishi & Hirokazu Yanagihara.
    Spatio-temporal adaptive fused Lasso for proportion data.
    The 13th KES International Conference on Intelligent Decision Technologies, Invited Session: Spatial Data Analysis and Sparse Estimation.
    KES Virtual Conference Centre. 2021/6/14 -- 16.
  2. Mineaki Ohishi, Kensuke Okamura, Yoshimichi Itoh & Hirokazu Yanagihara.
    Optimizations for categorizations of explanatory variables in linear regression via generalized fused Lasso.
    The 13th KES International Conference on Intelligent Decision Technologies, Invited Session: Spatial Data Analysis and Sparse Estimation.
    KES Virtual Conference Centre. 2021/6/14 -- 16. (Video).
  3. Keisuke Fukui, Mineaki Ohishi, Mariko Yamamura & Hirokazu Yanagihara.
    A fast optimization method for additive model via partial generalized ridge regression.
    The 12th KES International Conference on Intelligent Decision Technologies, Invited Session: High-Dimensional Data Analysis and Its Applications.
    KES Virtual Conference Centre. 2020/6/17 -- 19.
  4. Mineaki Ohishi, Hirokazu Yanagihara & Hirofumi Wakaki.
    Optimization of generalized Cp criterion for selecting ridge parameters in generalized ridge regression.
    The 12th KES International Conference on Intelligent Decision Technologies, Invited Session: High-Dimensional Data Analysis and Its Applications.
    KES Virtual Conference Centre. 2020/6/17 -- 19. (Video).
  5. Mineaki Ohishi & Hirokazu Yanagihara.
    A fast algorithm for solving model selection criterion minimization problem in generalized ridge.
    IMS - Asia Pacific Rim Meeting 2018, Contributed Paper Sessions.
    National University of Singapore. 2018/6/26 -- 29. (Oral).
  6. Mineaki Ohishi & Hirokazu Yanagihara.
    Equivalence under optimal regularization parameters between generalized ridge and adaptive-Lasso estimates in linear regression with orthogonal explanatory variables.
    Hiroshima Statistics Study Group seminars.
    Radiation Effects Research Foundation, Hiroshima. 2017/11/24. (Oral).
  7. Mineaki Ohishi & Hirokazu Yanagihara.
    Equivalence between adaptive-Lasso and generalized ridge estimates in linear regression with orthogonal explanatory variables after optimizing regularization parameters.
    Capital Normal University-Hiroshima University Joint conference on Mathematics.
    Capital Normal University, Beijing, China. 2017/9/21 -- 22. (Oral).

  ‘“à”­•\ (Šw‰ï):

  1. ‘åÎ•ôôE‰ª‘ºŒ’‰îEˆÉ“¡‰Ã“¹Eú匴G˜a.
    Generalized fused Lasso‚É‚æ‚éà–¾•Ï”‚̃JƒeƒSƒŠ‚̍œK‰».
    2021”N“x“ŒvŠÖ˜AŠw‰ï˜A‡‘å‰ï, ‹óŠÔ“Œv.
    ƒIƒ“ƒ‰ƒCƒ“. 2021/9/5 -- 9. (Œû“ª).
  2. ‘åÎ•ôôEŽR‘º–ƒ—ŽqEú匴G˜a.
    ƒƒWƒXƒeƒBƒbƒN‰ñ‹Aƒ‚ƒfƒ‹‚É‚¨‚¯‚é generalized fused Lasso ‚̍À•W~‰º–@.
    ‘æ15‰ñ“ú–{“ŒvŠw‰ït‹GW‰ï, ƒ|ƒXƒ^[ƒZƒbƒVƒ‡ƒ“.
    ƒIƒ“ƒ‰ƒCƒ“. 2021/3/8 -- 13. (ƒ|ƒXƒ^[).
    —DG”­•\ÜE“ŒvŒŸ’èƒZƒ“ƒ^[’·ÜŽóÜ.
  3. —é–Ø—T–çE‘åÎ•ôôE¬“c—½–çEú匴G˜a.
    Best subset selection in multivariate linear regressions via discrete first-order algorithms.
    2019”N“x“ŒvŠÖ˜AŠw‰ï˜A‡‘å‰ï, ƒ‚ƒfƒ‹‘I‘ðE³‘¥‰»–@.
    Ž ‰ê‘åŠw•FªƒLƒƒƒ“ƒpƒX. 2019/9/8 -- 12.
  4. •ŸˆäŒh—CE‘åÎ•ôôE¬“c—½–çE‰ª‘ºŒ’‰îEˆÉ“¡‰Ã“¹Eú匴G˜a.
    Variable selection method for nonparametric varying coefficient model via group lasso penalty.
    2019”N“x“ŒvŠÖ˜AŠw‰ï˜A‡‘å‰ï, ‹óŠÔ“Œvˆê”Ê.
    Ž ‰ê‘åŠw•FªƒLƒƒƒ“ƒpƒX. 2019/9/8 -- 12.
  5. ‘åÎ•ôôE•ŸˆäŒh—SE‰ª‘ºŒ’‰îEˆÉ“¡‰Ã“¹Eú匴G˜a.
    Estimation of geographically varying coefficient model via group fused Lasso.
    2019”N“x“ŒvŠÖ˜AŠw‰ï˜A‡‘å‰ï, ƒRƒ“ƒyƒeƒBƒVƒ‡ƒ“ƒZƒbƒVƒ‡ƒ“.
    Ž ‰ê‘åŠw•FªƒLƒƒƒ“ƒpƒX. 2019/9/8 -- 12. (Œû“ª).
  6. ‘åÎ•ôôE•ŸˆäŒh—SE‰ª‘ºŒ’‰îEˆÉ“¡‰Ã“¹Eú匴G˜a.
    ƒ}ƒ“ƒVƒ‡ƒ“‚Ì’À—¿‚ɑ΂·‚é’nˆæŒø‰Ê‚̐„’è–@‚Ì”äŠr.
    s“®Œv—ÊŠw‰ï‰ªŽR’nˆæ•”‰ï‘æ71‰ñŒ¤‹†‰ïE‘æ172‰ñ‰ªŽR“ŒvŒ¤‹†‰ï.
    ‰ªŽR—‰È‘åŠw. 2019/3/16. (Œû“ª).
    —DGÜŽóÜ.
  7. ‘åÎ•ôôE•ŸˆäŒh—SE‰ª‘ºŒ’‰îEˆÉ“¡‰Ã“¹Eú匴G˜a.
    Fused Lasso‚ð—p‚¢‚½’nˆæ•ª—Þ `ƒ}ƒ“ƒVƒ‡ƒ“‚Ì’À—¿‚ɑ΂·‚é’nˆæŒø‰Ê‚̃‚ƒfƒŠƒ“ƒO`.
    2018”N“x“ŒvŠÖ˜AŠw‰ï˜A‡‘å‰ï, ƒRƒ“ƒyƒeƒBƒVƒ‡ƒ“ƒZƒbƒVƒ‡ƒ“.
    ’†‰›‘åŠwŒãŠy‰€ƒLƒƒƒ“ƒpƒX. 2018/9/9 -- 13. (Œû“ª).
  8. ‘åÎ•ôô.
    Fused Lasso‚É‚æ‚éƒ}ƒ“ƒVƒ‡ƒ“’À—¿‚Ì’nˆæŒø‰ÊƒNƒ‰ƒXƒ^ƒŠƒ“ƒO.
    s“®Œv—ÊŠw‰ï‰ªŽR’nˆæ•”‰ï‘æ67‰ñŒ¤‹†‰ïE‘æ167‰ñ‰ªŽR“ŒvŒ¤‹†‰ï.
    ‰ªŽR—‰È‘åŠw. 2018/3/17. (Œû“ª).
    —DGÜŽóÜ.
  9. ‘åÎ•ôôE•ŸˆäŒh—SE‰ª‘ºŒ’‰îEˆÉ“¡‰Ã“¹Eú匴G˜a.
    Clustering of regional effects in apartment rents by fused Lasso.
    ‘æ12‰ñ“ú–{“ŒvŠw‰ït‹GW‰ï, ƒ|ƒXƒ^[ƒZƒbƒVƒ‡ƒ“.
    ‘ˆî“c‘åŠw‘ˆî“cƒLƒƒƒ“ƒpƒX. 2018/3/4. (ƒ|ƒXƒ^[).
  10. ‘åÎ•ôôEú匴G˜a.
    Equivalence between adaptive-Lasso and generalized ridge estimates in linear regression with orthogonal explanatory variables after optimizing regularization parameters.
    2017”N“x“ŒvŠÖ˜AŠw‰ï˜A‡‘å‰ï, ƒRƒ“ƒyƒeƒBƒVƒ‡ƒ“ƒZƒbƒVƒ‡ƒ“.
    “ìŽR‘åŠw–¼ŒÃ‰®ƒLƒƒƒ“ƒpƒX. 2017/9/3 -- 6. (Œû“ª).
  11. ‘åÎ•ôô.
    ’¼Œð‚·‚éà–¾•Ï”‚̉º‚ł̐üŒ`‰ñ‹Aƒ‚ƒfƒ‹‚É‚¨‚¯‚éˆê”ʉ»ƒŠƒbƒWŒ^L2ƒyƒiƒ‹ƒeƒB‚ÆAdaptive-LassoŒ^L1ƒyƒiƒ‹ƒeƒB‚ł̍œK‚ȉñ‹AŒW”‚Ì“¯“™«.
    s“®Œv—ÊŠw‰ï‰ªŽR’nˆæ•”‰ï‘æ63‰ñŒ¤‹†‰ïE‘æ163‰ñ‰ªŽR“ŒvŒ¤‹†‰ï.
    ‰ªŽR—‰È‘åŠw. 2017/3/18. (Œû“ª).
    —DGÜŽóÜ.
  12. ‘åÎ•ôôEú匴G˜a.
    Equivalence between optimized regression coefficients by adaptive-Lasso type L1 penalty and generalized ridge type L2 penalty in linear regression with orthogonal explanatory variables.
    ‘æ11‰ñ“ú–{“ŒvŠw‰ït‹GW‰ï, ƒ|ƒXƒ^[ƒZƒbƒVƒ‡ƒ“.
    ­ôŒ¤‹†‘åŠw‰@‘åŠw. 2017/3/5. (ƒ|ƒXƒ^[).
  13. ‘åÎ•ôôEú匴G˜aE“¡‰zNj.
    ˆê”ʉ»ƒŠƒbƒW‰ñ‹A‚É‚¨‚¯‚郊ƒbƒWƒpƒ‰ƒ[ƒ^‘I‘ð‚Ì‚½‚߂̏î•ñ—Ê‹K€Å¬‰»–â‘è‚̉ðÍ‰ð.
    2016”N“x“ŒvŠÖ˜AŠw‰ï˜A‡‘å‰ï, ƒ‚ƒfƒ‹‘I‘ð.
    ‹à‘ò‘åŠwŠpŠÔƒLƒƒƒ“ƒpƒX. 2016/9/4 -- 7. (Œû“ª).

  ‘“à”­•\ (‚»‚Ì‘¼):

  1. ‘åÎ•ôôEŽR‘º–ƒ—ŽqEú匴G˜a.
    Generalized fused LassoƒƒWƒXƒeƒBƒbƒN‰ñ‹A‚̍œK‰»‚ÆŽž‹óŠÔ•ªÍ.
    2021”N“x‹à—jƒZƒ~ƒi[, L“‡“ŒvƒOƒ‹[ƒv.
    L“‡‘åŠw. 2021/7/2. (ƒIƒ“ƒ‰ƒCƒ“).
  2. —é–Ø—T–çE‘åÎ•ôôE¬“c—½–çEú匴G˜a.
    ‘½•Ï—ʐüŒ`‰ñ‹A‚É‚¨‚¯‚édiscrete first-order algorithm‚ð—p‚¢‚½•Ï”‘I‘ð–@‚Ì’ñˆÄ.
    2019”N“x‹à—jƒZƒ~ƒi[, L“‡“ŒvƒOƒ‹[ƒv.
    L“‡‘åŠw. 2019/12/20.
  3. ‘åÎ•ôôE•ŸˆäŒh—SE‰ª‘ºŒ’‰îEˆÉ“¡‰Ã“¹Eú匴G˜a.
    •Î‚è‚Ì‚ ‚é‹óŠÔƒf[ƒ^‚ɑ΂·‚é‹óŠÔŒø‰Ê‚̐„’è–@.
    “ŒvƒTƒ}[ƒZƒ~ƒi[2019.
    ‘–¯hŽÉ‚Ђт«, •Ÿ‰ª. 2019/8/5 -- 8. (Œû“ª).
  4. ‘åÎ•ôôE•ŸˆäŒh—SE‰ª‘ºŒ’‰îEˆÉ“¡‰Ã“¹Eú匴G˜a.
    generalized Lasso‚ð—p‚¢‚½’nˆæŒø‰Ê‚̃Nƒ‰ƒXƒ^ƒŠƒ“ƒO.
    2018”N“x‹à—jƒZƒ~ƒi[, L“‡“ŒvƒOƒ‹[ƒv.
    L“‡‘åŠw. 2019/2/8. (Œû“ª).
  5. ‘åÎ•ôô.
    ”±‘¥•t‚«„’è–@‚̐³‘¥‰»ƒpƒ‰ƒ[ƒ^Å“K‰».
    “ŒvƒTƒ}[ƒZƒ~ƒi[2017.
    ‹S“{ìƒp[ƒNƒzƒeƒ‹ƒY, “È–Ø. 2017/8/5 -- 8. (Œû“ª).
  6. ‘åÎ•ôôEú匴G˜a.
    ƒŠƒbƒWƒpƒ‰ƒ[ƒ^‘I‘ð‚Ì‚½‚ß‚ÌGCVÅ¬‰»–â‘è‚É‚¨‚¯‚é”±‘¥€‚Ì”äŠr.
    Bayes Inference and Its Related Topics Œ¤‹†‰ï, RIMS‹¤“¯Œ¤‹†.
    ‹ž“s‘åŠw”—‰ðÍŒ¤‹†Š. 2017/3/6 -- 8. (Œû“ª).
  7. ‘åÎ•ôôEú匴G˜aE“¡‰zNj.
    ˆê”ʉ»ƒŠƒbƒW‰ñ‹A‚É‚¨‚¯‚郊ƒbƒWƒpƒ‰ƒ[ƒ^‘I‘ð‚Ì‚½‚߂̏î•ñ—Ê‹K€Å¬‰»–â‘è.
    2016”N“x‹à—jƒZƒ~ƒi[, L“‡“ŒvƒOƒ‹[ƒv.
    L“‡‘åŠw. 2016/12/16. (Œû“ª).
  8. ‘åÎ•ôô.
    Žå¬•ª‰ñ‹A‚É‚¨‚¯‚éLasso‚̃`ƒ…[ƒjƒ“ƒOƒpƒ‰ƒ[ƒ^‘I‘ð‚Ì‚½‚ß‚ÌGCVÅ¬‰»–â‘è.
    Œ¤‹†W‰ï "“Œv“I„˜_‚É‚¨‚¯‚éÅ‹ß‚Ì“WŠJ".
    ‹{“‡ƒR[ƒ‰ƒ‹ƒzƒeƒ‹, L“‡. 2016/12/4 -- 5. (Œû“ª).