matthew beal altizcsebuffaloedu call participation open problems challenges nonparametric bayesian methods machine learning workshop 2005 neural information processing systems nips conference friday december 9 2005 whistler bc canada httpaluminumcsebuffaloedu8080npbayesnipsws05 deadline submissions monday october 31 2005 notification decision friday november 4 2005 organizers matthew j beal university buffalo state university new york yee whye teh national university singapore overview two years ago nips workshops hosted first forum discussion nonparametric bayesian npb methods infinite models used machine learning brought bear techniques statistical disciplines perennial problems nips community model size structure selection bayesian viewpoint npb methods include topics heavily studied nips community gaussian processes recently dirichlet process mixture models topics starting make impact npb methods attractive machine learning practitioners powerful flexible key ingredient npb formalism sidestep traditional practice parameter fitting instead integrating possibly complex parameters model allows interesting situations exist model countably infinite components mixture model infinite topics topic model infinite dimensions hidden state space also flexibility npb model often useful domains difficult articulate priors likelihood functions text language modeling spatiallydependent process modeling etc since last workshop models sparse factor analysis latent dirichlet allocation hidden markov models even robot mapping tasks benefitted flexibility nonparametric bayesian approach nonparametric alternatives shown give superior generalization performance compared finite model selection techniques time two years collect together various research directions use define delineate challenges facing nonparametric bayesian community set open problems stand reasonable chance solved focussed research plans workshop focus less wellstudied topics like gaussian processes potential new ideas statistics community end number experts nonparametric bayesian methods statistics share experiences expertise general nips community effort transfer build upon key methodologies developed since time ripe two groups coalesce particular would like workshop address new techniques techniques methodologies currently used statistics communities transferred used machine learning applications conversely techniques developed withing nips community interest general statistics communities new problems still wide variety problems nips community elegantly solved nonparametric bayesian models example problems needing smoothly timevarying spatiallyvarying processes would useful identify problems necessary characteristics nonparametric solutions serve concrete goals research computationalinferential issues inference nonparametric models part carried using expensive mcmc sampling recently variational expectation propagation methods applied isolated cases popular use models need efficient reliable inference schemes methods scale high dimensional data large databases email repositories news reports worldwideweb could npb methods much work little benefit format oneday workshop designed highly interactive consisting 3 4 themed sessions short talks lengthy moderated discussion periods anticipate strong response likely poster session morning afternoon sessions attracted several statisticians outside nips beaten track also distinguished panel statisticiansmachine learners discuss points arising workshops discussions call contributions potential speakersdiscussantsattendees encouraged submit extended abstracts 24 pages length outlining research relates themes monday october 31 looking position papers extensions theory applications well case studies nonparametric methods overwhelming response accommodate poster session afternoon break morning evening sessions chosen abstracts posted workshop website beforehand stimulate discussion deadline submissions monday october 31 2005 notification decision friday november 4 2005 date workshop friday december 9 2005 please email submissions altizcsebuffaloedu andor qyrcfivgmailcom encourage log post questions contribute pages workshop website httpaluminumcsebuffaloedu8080npbayesnipsws05 wiki format either person anonymously login wish modify pages please contact us permissions website intended serve resource nonparametric bayesians workshop already contains plenty links literature npb within outside nips community input tailor workshop according suggestions also give comprehensive feedback topics covered provided survey form httpaluminumcsebuffaloedu8080npbayesnipsws05survey free fill partially anonymously desired thank matt beal yee whye teh wed 06 aug 2008 141203 0400 uai cfp nips 2005 workshop nonparametric bayesian methods