Submodular Functions And Optimization Submodular Functions And Optimization - jcaamerik.ml

icml 2011 the 28th international conference on machine - contents awards printed proceedings online proceedings cross conference papers awards in honor of its 25th anniversary the machine learning journal is sponsoring the awards for the student authors of the best and distinguished papers, submodularity org submodularity org tutorials - submodularity org is tracked by us since may 2017 over the time it has been ranked as high as 1 305 299 in the world all this time it was owned by carlos guestrin it was hosted by carnegie mellon university and godaddy com llc submodularity has the lowest google pagerank and bad results in terms of yandex topical citation index, combinatorial optimization 3 volume a b c - an in depth overview of polyhedral methods and efficient algorithms in combinatorial optimization these methods form a broad coherent and powerful kernel in combinatorial optimization with strong links to discrete mathematics mathematical programming and computer science, ipco information mathematical optimization society - ipco information information collected by gerhard woeginger contents what is ipco what is the scope of ipco which type of paper is appropriate for ipco the timing of ipco, homepage of yuval filmus - we consider the np complete optimization problem bandwith anupam gupta gave an o log 2 5 n approximation algorithm for trees and showed that his algorithm has an approximaion ratio of o log n on caterpillars trees composed of a central path and paths emanating from it we show that the same approximation ratio is obtained on trees composed of a central path and caterpillars emanating from it, john schulman s homepage joschu net - john schulman welcome to my stodgy academic homepage i m a research scientist at openai where i work on reinforcement learning rl especially related to transfer learning and meta learning i lead the games team where we mostly use games as a testbed for reinforcement learning and we ve developed the gym retro dataset based on classic video games, aaron sidford s homepage - aaron sidford about me i am an assistant professor of management science and engineering and by courtesy of computer science at stanford university, colt18 for authors learning theory - learning linear predictors with the logistic loss both in stochastic and online settings is a fundamental task in learning and statistics with direct connections to classification and boosting, nips 2018 call for papers - do not remove this comment is monitored to verify that the site is working properly, professor ming jer tsai mdclab - ming jer tsai professor ph d national taiwan university department of computer science national tsing hua university hsinchu taiwan, iccv 2013 papers on the web computer vision resource - oral 3d computer vision elastic fragments for dense scene reconstruction project pdf qian yi zhou stanford university stephen miller stanford university vladlen koltun stanford university, technical reports department of computer science - title authors published abstract publication details easy email encryption with easy key management john s koh steven m bellovin jason nieh, hisashi kashima s machine learning research - go to japanese page hisashi kashima professor kyoto university japan hisashi kashima is a professor at department of intelligence science and technology kyoto university