Traditional DSL troubleshooting solutions are reactive, relying mainly on customers to report problems, and tend to be labor-intensive, time consuming, prone to incorrect resoluti...
Yu Jin, Nick G. Duffield, Alexandre Gerber, Patric...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Abstract. Cassava Mosaic Disease (CMD) has been an increasing concern to all countries in sub-Saharan Africa that depend on cassava for both commercial and local consumption. Infor...
We present a numerical approximation technique for the analysis of continuous-time Markov chains that describe networks of biochemical reactions and play an important role in the ...
Thomas A. Henzinger, Maria Mateescu, Linar Mikeev,...
We present a simple randomized algorithmic framework for connected facility location problems. The basic idea is as follows: We run a black-box approximation algorithm for the unc...
Friedrich Eisenbrand, Fabrizio Grandoni, Thomas Ro...