| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
|
|---|
| Outline of Final Research Achievements |
We regard the online decision problem based on relative comparison as an optimization problem with discrete structures and study not only the problem itself but also related areas such as learning theory, optimization and online prediction. In particular, our project mainly focuses on (1) analyses of generalization ability for prediction of low-rank matrices (2) online/offline optimization methods for scheduling problems with precedence constraints, (3) machine learning algorithms over compressed data, and so on. Among them, our result on machine leaning over compressed data won the best paper award at WALCOM2018, a conference on algorithms.
|
