22 citations · 136 across the 44 of their papers we have counts for
5 papers · 1 filter
Optimistic Robust Optimization With Applications To Machine Learning
Matthew Norton, Akiko Takeda, Alexander Mafusalov
Robust Optimization has traditionally taken a pessimistic, or worst-case viewpoint of uncertainty which is motivated by a desire to find sets of optimal policies that maintain feas…
A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems
Tianxiang Liu, Ting Kei Pong, Akiko Takeda
We consider a class of nonconvex nonsmooth optimization problems whose objective is the sum of a smooth function and a finite number of nonnegative proper closed possibly nonsmooth…
Boosting the kernelized shapelets: Theory and algorithms for local features
Daiki Suehiro, Kohei Hatano, Eiji Takimoto +3
We consider binary classification problems using local features of objects. One of motivating applications is time-series classification, where features reflecting some local close…
Trimmed Density Ratio Estimation
Song Liu, Akiko Takeda, Taiji Suzuki +1
Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation procedure can be…
Efficient DC Algorithm for Constrained Sparse Optimization
Katsuya Tono, Akiko Takeda, Jun-ya Gotoh
We address the minimization of a smooth objective function under an -constraint and simple convex constraints. When the problem has no constraints except the -const…