{"content":[{"text":"Contributions","bbox":{"left":0.05888,"top":0.149789,"width":0.211714,"height":0.050682000000000005}},{"text":"1","bbox":{"left":0.920279,"top":0.164747,"width":0.015191999999999983,"height":0.04317399999999999}},{"text":"MaxHedge: Maximizing a Maximum Online \n Stephen Pasteris, Fabio Vitale, Kevin Chan, Shiqiang Wang, Mark Herbster","bbox":{"left":0.089979,"top":0.316007,"width":0.8094189999999999,"height":0.08082800000000001}},{"text":"•","bbox":{"left":0.070712,"top":0.452066,"width":0.008316000000000004,"height":0.03754200000000002}},{"text":"Paper published in AISTATS 2019","bbox":{"left":0.101071,"top":0.452066,"width":0.36428000000000005,"height":0.03754200000000002}},{"text":"•","bbox":{"left":0.070712,"top":0.527898,"width":0.008316000000000004,"height":0.037541999999999964}},{"text":"Service (server) placement problems well studied in the batch (static) case","bbox":{"left":0.101071,"top":0.527898,"width":0.805215,"height":0.037541999999999964}},{"text":"•","bbox":{"left":0.070712,"top":0.601713,"width":0.008316000000000004,"height":0.037541999999999964}},{"text":"We present a novel online framework based on online machine learning","bbox":{"left":0.101071,"top":0.601713,"width":0.7979849999999999,"height":0.037541999999999964}},{"text":"•","bbox":{"left":0.070712,"top":0.675529,"width":0.008316000000000004,"height":0.037541999999999964}},{"text":"Framework is very general in that it has different problems as subcases","bbox":{"left":0.101071,"top":0.675529,"width":0.76544,"height":0.037541999999999964}},{"text":"•","bbox":{"left":0.070712,"top":0.75136,"width":0.008316000000000004,"height":0.037541999999999964}},{"text":"We give an efficient algorithm, MaxHedge, with a performance guarantee","bbox":{"left":0.101071,"top":0.75136,"width":0.7841210000000001,"height":0.037541999999999964}}],"images_bbox":[{"left":0.915414,"top":0.785483,"width":0.063861,"height":0.090347}],"images_bbox_not_text_overlap":[{"left":0.915414,"top":0.785483,"width":0.063861,"height":0.090347}]}