Travelled to:1 × Australia
1 × Finland
1 × France
1 × Israel
1 × United Kingdom
2 × Canada
2 × China
4 × USA
Collaborated with:T.Cohen S.Ahn D.P.Kingma Y.Chen Y.W.Teh A.K.Balan ∅ P.Smyth T.Salimans B.Shahbaba R.Gomes P.Perona P.V.Gehler A.Holub M.Rosen-Zvi A.Korattikara N.Liu S.Rajan J.R.Foulds L.Boyles C.DuBois I.Porteous D.Newman A.T.Ihler A.U.Asuncion
Talks about:stochast (5) gradient (5) infer (5) bayesian (4) model (3) learn (3) dynam (3) mcmc (3) distribut (2) dirichlet (2)
Person: Max Welling
DBLP: Welling:Max
Contributed to:
2015
201520142014201320122011201020092008200820062004Wrote 16 papers:
- ICML-2015-CohenW #exponential #product line
- Harmonic Exponential Families on Manifolds (TC, MW), pp. 1757–1765.
- ICML-2015-SalimansKW #markov #monte carlo
- Markov Chain Monte Carlo and Variational Inference: Bridging the Gap (TS, DPK, MW), pp. 1218–1226.
- KDD-2015-AhnKLRW #distributed #matrix #probability #scalability #using
- Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC (SA, AK, NL, SR, MW), pp. 9–18.
- ICML-c1-2014-BalanCW
- Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget (AKB, YC, MW), pp. 181–189.
- ICML-c2-2014-AhnSW #distributed #probability
- Distributed Stochastic Gradient MCMC (SA, BS, MW), pp. 1044–1052.
- ICML-c2-2014-CohenW #commutative #learning
- Learning the Irreducible Representations of Commutative Lie Groups (TC, MW), pp. 1755–1763.
- ICML-c2-2014-KingmaW #performance
- Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets (DPK, MW), pp. 1782–1790.
- KDD-2013-FouldsBDSW #probability
- Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation (JRF, LB, CD, PS, MW), pp. 446–454.
- ICML-2012-AhnBW #probability
- Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring (SA, AKB, MW), p. 230.
- ICML-2011-WellingT #learning #probability
- Bayesian Learning via Stochastic Gradient Langevin Dynamics (MW, YWT), pp. 681–688.
- ICML-2010-ChenW #modelling
- Dynamical Products of Experts for Modeling Financial Time Series (YC, MW), pp. 207–214.
- ICML-2009-Welling
- Herding dynamical weights to learn (MW), pp. 1121–1128.
- ICML-2008-GomesWP #bound #memory management #modelling #topic
- Memory bounded inference in topic models (RG, MW, PP), pp. 344–351.
- KDD-2008-PorteousNIASW #performance
- Fast collapsed gibbs sampling for latent dirichlet allocation (IP, DN, ATI, AUA, PS, MW), pp. 569–577.
- ICML-2006-GehlerHW #adaptation #information retrieval #recognition
- The rate adapting poisson model for information retrieval and object recognition (PVG, AH, MW), pp. 337–344.
- ICML-2004-WellingRT #approximate #markov
- Approximate inference by Markov chains on union spaces (MW, MRZ, YWT).