Standard Reinforcement Learning (RL) aims to optimize decision-making rules in terms of the expected return. However, especially for risk-management purposes, other criteria such ...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...
We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In literature, MKL is often solved by an alternating approach: (1) the minimization of ...
Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Mic...
We have developed a system architecture, measuring and modeling techniques, and algorithms for on-line power and energy optimization and thermal management. The starting point for...
—Current general-purpose memory allocators do not provide sufficient speed or flexibility for modern highperformance applications. To optimize metrics like performance, memory us...