Maximization of the Ratio of Two Convex Quadratic Functions over a Polytope. - Jun-ya Gotoh; Hiroshi Konno (Comput. Optim. Appl., 2001)

Bounding Option Prices by Semidefinite Programming: A Cutting Plane Algorithm. - Jun-ya Gotoh; Hiroshi Konno (Manag. Sci., 2002)

Global optimization method for solving the minimum maximal flow problem. - Jun-ya Gotoh; Nguyen Van Thoai; Yoshitsugu Yamamoto (Optim. Methods Softw., 2003)

Minimal Ellipsoid Circumscribing a Polytope Defined by a System of Linear Inequalities. - Jun-ya Gotoh; Hiroshi Konno (J. Glob. Optim., 2006)

Newsvendor solutions via conditional value-at-risk minimization. - Jun-ya Gotoh; Yuichi Takano (Eur. J. Oper. Res., 2007)

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

α-Conservative approximation for probabilistically constrained convex programs. - Yuichi Takano; Jun-ya Gotoh (Comput. Optim. Appl., 2010)

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

Bounding Contingent Claim Prices via Hedging Strategy with Coherent Risk Measures. - Jun-ya Gotoh; Yoshitsugu Yamamoto; Weifeng Yao (J. Optim. Theory Appl., 2011)

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

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)

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

Multi-period portfolio selection using kernel-based control policy with dimensionality reduction. - Yuichi Takano; Jun-ya Gotoh (Expert Syst. Appl., 2014)

Two pairs of families of polyhedral norms versus ℓ p -norms: proximity and applications in optimization. - Jun-ya Gotoh; Stan Uryasev (Math. Program., 2016)

Support vector machines based on convex risk functions and general norms. - Jun-ya Gotoh; Stan Uryasev (Ann. Oper. Res., 2017)

Preface. - Stan Uryasev; Jun-ya Gotoh (Ann. Oper. Res., 2018)

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

Robust empirical optimization is almost the same as mean-variance optimization. - Jun-ya Gotoh; Michael Jong Kim; Andrew E. B. Lim (Oper. Res. Lett., 2018)

Worst-case sensitivity. - Jun-ya Gotoh; Michael Jong Kim; Andrew E. B. Lim (CoRR, 2020)

A data-driven approach to beating SAA out-of-sample. - Jun-ya Gotoh; Michael Jong Kim; Andrew E. B. Lim (CoRR, 2021)

Calibration of Distributionally Robust Empirical Optimization Models. - Jun-ya Gotoh; Michael Jong Kim; Andrew E. B. Lim (Oper. Res., 2021)

On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression. - Shummin Nakayama; Jun-ya Gotoh (Optim. Lett., 2021)

Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension. - Shotaro Yagishita; Jun-ya Gotoh (J. Mach. Learn. Res., 2024)

Technical Note - A Data-Driven Approach to Beating SAA Out of Sample. - Jun-ya Gotoh; Michael Jong Kim; Andrew E. B. Lim (Oper. Res., 2025)

Distributionally Robust Optimization is a Multi-Objective Problem. - Jun-ya Gotoh; Michael Jong Kim; Andrew E. B. Lim (CoRR, 2025)