This page gives a complete overview of all scientific publications on Machine Learning topics of Scyfer’s co-founder Prof. Max Welling and his scientific colleagues.

Max Welling - Scyfer
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This material is based upon work supported by the National Science Foundation under Grant No. 0447903. and Grant No. 0535278. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

Working Papers

  • T. Meeds and M. Welling(2014)
    GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
    arXiv:1401.2838 [pdf]

Published Papers

  • T. Cohen and M. Welling(2014)
    Learning the Irreducible Representations of Commutative Lie Groups
    ICML 2014 [pdf, supp.mat.]
  • D. Kingma and M. Welling(2014)
    Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets,
    ICML 2014 [pdf]
  • S. Ahn, B. Shahbaba and M. Welling(2014)
    Distributed Stochastic Gradient MCMC
    ICML 2014 [pdf]
  • D. Kingma and M. Welling(2014)
    Auto-Encoding Variational Bayes,
    ICLR 2014 [pdf]
  • A. Korattikara, Y. Chen and M. Welling(2014)
    Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
    ICML 2014 [pdf]
  • M. Welling(2014)
    Exploiting the Statistics of Learning and Inference
    Proceedings of the NIPS 2014 Workshop on “Probabilistic Models for Big Data” [pdf]
  • M. Welling(2014)
    Inaugural Speech (in Dutch)
    University of Amsterdam 2014 [slides ppt][slides pdf][written text][spoken text][video]
  • Y. Chen, A. Gelfand and M. Welling(2014)
    Herding for Structured Prediction
    In: Advanced Structured Prediction, S.Nowozin, P.Gehler, J.Jancsary, C. Lampert (Eds) 2014 [pdf]
  • C. Dubois, A. Korattikara and M. Welling(2014)
    Approximate Slice Sampling for Bayesian Posterior Inference
    AISTATS 2014 [pdf]
  • J, Foulds, L. Boyles, C. Dubois, P, Smyth and M. Welling (2013)
    Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation
    KDD 2013 [pdf arXiv]
  • L. Bornn, Y. Chen, N. de Freitas, M. Eskelin, J. Fang and M. Welling (2013)
    Herded Gibbs Sampling
    ICLR 2013 [pdf]
  • P. Welinder, M. Welling and P. Perona (2013)
    Semisupervised Classifier Evaluation and Recalibration
    CVPR 2013 [pdf]
  • S. Ahn, Y. Chen and M. Welling (2013)
    Distributed and Adaptive Darting Monte Carlo through Regenerations
    AISTATS 2013 [pdf]
  • Y. Chen and M. Welling (2013)
    Evidence Estimation for Partially Observed MRFs
    AISTATS 2013 [pdf]
  • L. Boyles and M. Welling (2012)
    The Time-Marginalized Coalescent Prior for Hierarchical Clustering
    NIPS 2012 [paper:pdf][suppl.mat.:pdf]
  • M. Welling, A. Gelfand and A. Ihler (2012)
    A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation
    UAI 2012 [pdf]
  • A. Gelfand and M. Welling (2012)
    Generalized Belief Propagation on Tree Robust Structured Region Graphs
    UAI 2012 [pdf]
  • Y. Chen and M. Welling (2012)
    Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
    UAI 2012 [pdf]
  • S. Ahn, A. Korattikara and M. Welling (2012)
    Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
    ICML 2012 [pdf][Google Talk]
    Winner of the ICML 2012 Best Paper Award.
  • M. Welling, I. Porteous and K. Kurihara (2012)
    Exchangeable Inconsistent Priors for Bayesian Posterior Inference
    Workshop on Information Theory and Applications (ITA) 2012 [pdf]
  • X. Zhu, J. Lowengrub and M. Welling (2012)
    Predicting Simulation Parameters of Biological Systems using a Gaussian Process Model
    JSM & Special Issue of the Stat. Analysis and data Mining Journal 2012 [pdf]
    Winner of the ASA SDLM Student Paper Competition,2012
  • D. Gorur, L. Boyles and M. Welling (2011)
    Scalable Inference on Kingman?s Coalescent using Pair Similarity
    AISTATS 2012 [pdf]
  • L. Boyles, A. Korattikara, D. Ramanan and M. Welling (2011)
    Statistical Tests for Optimization Efficiency
    NIPS 2011 [pdf][software]
  • Y. Chen, A. Gelfand, C. Fowlkes and M. Welling (2011)
    Integrating Local Classifiers through Nonlinear Dynamics on Label Graphs with an Application to Image Segmentation
    ICCV 2011 [pdf]
  • M. Welling and Y.W. Teh (2011)
    Bayesian Learning via Stochastic Gradient Langevin Dynamics
    ICML 2011 [pdf]
  • A. Korattikara, L. Boyles, J. Kim, H. Park and M. Welling (2011)
    Statistical Optimization for Nonnegative Matrix Factorization
    AISTATS 2011 [pdf]
  • L. Van Der Maaten, M. Welling and L.K. Saul (2011)
    Hidden-Unit Conditional Random Fields
    AISTATS 2011 [pdf] (software)
  • A. Asuncion, D. Newman, I. Porteous, S. Triglia, P. Smyth and M. Welling (2010)
    Distributed Gibbs Sampling for Latent Variable Models
    Bookchapter in: Scaling Up Machine Learning, Cambridge University Press (to appear)
  • E. Bart, M. Welling and P. Perona (2010)
    Unsupervised Organization of Image Collections: Taxonomies and Beyond
    Transactions on Pattern Analysis and Machine Intelligence [pdf] (TPAMI – to appear)
  • Alfred Kume and Max Welling (2010)
    Maximum-Likelihood Estimation for the Offset Normal Shape Distributions using EM
    Journal of Computational and Graphical Statistics, Vol. 19, No. 3: 702?723 [url][pdf]
  • A. Gelfand, L. Van Der Maaten, Y. Chen, M. Welling(2010)
    On Herding and the Perceptron Cycling Theorem
    NIPS 2010 [pdf]
  • Y. Chen, M. Welling and A. Smola(2010)
    Supersamples from Kernel-Herding
    UAI 2010 [pdf]
  • Y. Chen and M. Welling(2010)
    Dynamical Products of Experts for Modeling Financial Time Series
    ICML 2010 [pdf]
  • A. Asuncion, P. Smyth, M. Welling (2010)
    Asynchronous Distributed Estimation of Topic Models for Document Analysis
    Statistical Methodology 2010 [url]
  • I. Porteous, A. Asuncion, M. Welling (2010)
    Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures
    AAAI 2010 [pdf]
  • M. Welling and Y. Chen(2010)
    Statistical Inference Using Weak Chaos and Infinite Memory
    Proceedings of the Int’l Workshop on Statistical-Mechanical Informatics
    (IW-SMI 2010)[pdf][url]
  • Y. Chen and M. Welling(2010)
    Parametric Herding
    AISTATS 2010 [pdf]
  • Y. Zhang, L. Bao, S.H. Yang, M. Welling, Di Wu(2010)
    Localization Algorithms for Wireless Sensor Retrieval
    The Computer Journal 2010 [url]
  • Y. Zhang, L. Bao, M. Welling, S.H. Yang(2009)
    Base Station Localization in Search of Empty Spectrum Spaces for Cognitive Radio Networks
    Mobile Ad-hoc and Sensor Networks (MSN) 2009 [pdf]
  • D. Newman, A. Asuncion, P. Smyth, M. Welling(2009)
    Distributed Algorithm for Topic Models
    Journal Machine Learning Research 2009 [pdf]
  • M. Welling(2009)
    Herding Dynamic Weights for Partially Observed Random Field Models
    UAI 2009 [pdf] [Correction to proof of recurrence, thanks to Olivier Delalleau for pointing out the issue]
  • M. Welling(2009)
    Herding Dynamic Weights to Learn
    ICML 2009 [pdf] [Correction to proof of recurrence, thanks to Olivier Delalleau for pointing out the issue]
  • A. Asuncion, P. Smyth, M. Welling, Y.W. Teh(2009)
    On Smoothing and Inference for Topic Models
    UAI 2009 [pdf]
  • Y. Chen and M. Welling(2009)
    Bayesian Extreme Components Analysis
    IJCAI 2009 [pdf]
  • S.A. Cole, M. Welling, R.Dioso-Villa, R. Carpenter(2008)
    Beyond the Individuality of Fingerprints:
    A Measure of Simulated Computer Latent Print Source Attribution Accuracy
    Law, Probability and Risk 2008 [pdf]
  • A. Ascuncion, P. Smyth and M. Welling(2008)
    Asynchronous Distributed Learning of Topic Models
    NIPS 2008 [pdf]
  • I. Porteous, A. Ascuncion, D. Newman, A. Ihler, P. Smyth and M. Welling(2008)
    Fast Collapsed Gibbs Sampling For Latent Dirichlet Allocation
    KDD 2008 [pdf] [software]
  • Max Welling, Y.W. Teh and B. Kappen(2008)
    Hybrid Variational-MCMC Inference in Bayesian Networks
    UAI 2008 [pdf]
  • R. Gomes, M. Welling and P. Perona(2008)
    Memory Bounded Inference in Topic Models
    ICML 2008 [pdf]
  • Ian Porteous, Evgeniy Bart and Max Welling (2008)
    Multi-HDP: A Nonparametric Bayesian Model for Tensor Factorization
    AAAI 2008 [pdf]
  • Ryan Gomes, Max Welling and Pietro Perona(2008)
    Incremental Learning of Nonparametric Bayesian Mixture Models
    CVPR 2008 [pdf]
  • Evgeniy Bart, Ian Porteous, Pietro Perona and Max Welling (2008)
    Unsupervised Learning of Visual Taxonomies
    CVPR 2008 [pdf]
  • Max Welling, Chaitanya Chemudugunta and Nathan Sutter (2008)
    Deterministic Latent Variable Models and Their Pitfalls
    SIAM Conference on Data Mining SDM 2008 [pdf]
  • Kenichi Kurihara and Max Welling (2008)
    Bayesian K-Means as a ?Maximization-Expectation? Algorithm
    Neural Computation, accepted [pdf]
  • Max Welling, Ian Porteous and Evgeniy Bart (2007)
    Infinite State Bayesian Networks For Structured Domains
    NIPS 2007 [pdf]
  • Dave Newman, Arthur Ascuncion, Padhriac Smyth and Max Welling (2007)
    Distributed Inference for Latent Dirichlet Allocation
    NIPS 2007 [pdf]
  • Yee Whye Teh, Kenichi Kurihara and Max Welling (2007)
    Collapsed Variational Inference for HDP
    NIPS 2007 [pdf]
  • Max Welling (2007)
    Products of Experts
    ScholarPedia 2007 [pdf,url]
  • Alex Holub, Max Welling and Pietro Perona (2007)
    Hybrid Generative-Discriminative Object Recognition
    International Journal Computer Vision (IJCV) [pdf]
  • Max Welling and Joseph Lim (2007)
    SLEEP: Sensor Location Estimation with Expectation Propagation
    ICANN 2007[pdf,June’08, mistake corrected in Alg. Box 3I relative to published version]
  • Christian Sminchisescu and Max Welling (2007)
    Generalized Darting Monte Carlo
    AISTATS 2007 [pdf]
  • Kenichi Kurihara, Max Welling and Yee Whye Teh (2007)
    Collapsed Variational Dirichlet Process Mixture Models
    IJCAI 2007 [ps,pdf]
  • Sridevi Parise and Max Welling (2006)
    Structure Learning in Markov Random Fields
    NIPS 2006 [ps,pdf]
  • Yee Whye Teh, Dave Newman and Max Welling (2006)
    A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
    NIPS 2006 [ps,pdf]
  • Kenichi Kurihara, Max Welling and Nikos Vlassis (2006)
    Accelerated Variational DP mixture Models
    NIPS 2006 [ps,pdf]
  • Max Welling (2006)
    Flexible Priors for Infinite Mixture Models
    ICML workshop on Nonparametric Baysian methods 2006 [pdf]
  • Ian Porteous, Alex Ihler, Padhriac Smyth and Max Welling (2006)
    Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick-Breaking Representation
    UAI 2006 [pdf]
  • Max Welling and Sridevi Parise (2006)
    Bayesian Random Fields: The Bethe-Laplace Approximation
    UAI 2006 [pdf]
  • Peter Gehler, Alex Holub and Max Welling (2006)
    The Rate Adapting Poisson (RAP) model for Information Retrieval and Object Recognition.
    ICML 2006 [pdf,software]
  • Max Welling and Kenichi Kurihara (2005)
    Bayesian K-Means as a ?Maximization-Expectation? Algorithm
    SIAM Conference on Data Mining SDM2006 [pdf,tech-report,software]
  • Sridevi Parise and Max Welling (2005)
    Learning in Markov Random Fields: An Empirical Study
    Joint Statistical Meeting JSM2005 [pdf,software]
  • Geoffrey Hinton, Simon Osindero, Max Welling and Yee Whye Teh (2005)
    Unsupervised Discovery of Non-Linear Structure using Contrastive Back-Propagation
    Accepted in Cognitive Science 30(4) 2006 [pdf]
  • Simon Osindero, Max Welling and Geoffrey Hinton (2005)
    Topographic Product Models Applied to Natural Scene Statistics
    Neural Computation (accepted) [pdf]
  • Alex Holub, Max Welling and Pietro Perona (2005)
    Combining Generative Models and Fisher Kernels for Object Recognition
    ICCV 2005 [pdf]
  • Peter Gehler and max Welling (2005)
    Products of ?Edge-Perts?
    NIPS 2005 [pdf] [software]
  • Max Welling, Tom Minka and Yee Whye Teh (2005)
    Structured Region Graphs: Morphing EP into GBP.
    UAI 2005 [ps,pdf] (extended version with proofs)
  • Max Welling (2005)
    Robust Higher Order Statistics
    AISTATS 2005 [ps,pdf]
  • Max Welling (2005)
    An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions
    AISTATS 2005 [ps,pdf]
  • Max Welling & Charles Sutton (2005)
    Learning in Markov Random Fields with Contrastive Free Energies
    AISTATS 2005 [ps,pdf]
  • Max Welling, Michal Rosen-Zvi & Geoffrey Hinton (2004)
    Exponential Family Harmoniums with an Application to Information Retrieval
    NIPS 2004 [ps pdf]
  • Max Welling, Richard Zemel and Geoffrey Hinton (2003)
    Probabilistic Sequential Independent Components Analysis
    IEEE Transactions on Neural Networks [ps pdf]
  • Max Welling (2004)
    On the Choice of Regions for Generalized Belief Propagation
    UAI 2004 [ps pdf]
  • Max Welling, Michal Rosen-Zvi & Yee Whye Teh (2004)
    Approximate Inference by Markov Chains on Union Spaces
    ICML 2004 [ps pdf]
  • Max Welling & Yee Whye Teh (2002)
    Linear Response Algorithms for Approximate Inference in Graphical Models
    Neural Computation 16 [pdf,Feb.08 typo corrected-thanks to Vicen� G�mez]
  • Max Welling, Geoffrey Hinton and Andriy Mnih (2003)
    Wormholes Improve Contrastive Divergence
    NIPS 2003 [ps pdf]
  • Max Welling, Felix Agakov & Chris Williams (2003)
    Extreme Components Analysis
    NIPS 2003 [pdf]
  • Max Welling & Yee Whye Teh (2003)
    Linear Response for Approximate Inference
    NIPS 2003 [pdf,Feb.08 typo corrected-thanks to Vicen� G�mez]
  • Max Welling, Richard Zemel and Geoffrey Hinton (2003)
    Efficient Parametric Projection Pursuit Density Estimation
    UAI 2003 [ps]
  • Yee Whye Teh, Max Welling, Simon Osindero & Geoffrey Hinton (2003)
    Energy-Based Models for Sparse Overcomplete Representations
    JMLR [ps]
  • Yee Whye Teh & Max Welling (2003)
    On Improving the Efficiency of the Iterative Proportional Fitting Procedure
    AISTATS 2003 [ps]
  • Max Welling, Richard Zemel and Geoffrey Hinton (2002)
    Self-Supervised Boosting
    NIPS 2002 [ps]
  • Max Welling, Geoffrey Hinton and Simon Osindero (2002)
    Learning Sparse Topographic Representations with Products of Student-t Distributions
    NIPS 2002 [ps]
  • Max Welling & Yee Whye Teh (2001)
    Approximate Inference in Boltzmann Machines
    AIJ [ps]
  • Max Welling & Geoffrey Hinton (2002)
    A New Learning Algorithm for Mean Field Boltzmann Machines
    ICANN2002, Madrid [ps]
  • Geoffrey E. Hinton, Max Welling, Yee Whye Teh & Simon K. Osindero (2001)
    A New View of ICA
    Int. Conf. on Independent Component Analysis and Blind Source Separation, ICA2001, San Diego [ps]
  • Yee Whye Teh & Max Welling (2001)
    The Unified Propagation and Scaling Algorithm
    NIPS2001, Vancouver [ps]
  • Max Welling & Markus Weber(2001) 
    Positive Tensor Factorization 
    Pattern Recognition Letters 22 (12), pp. 1255-1261 [ps]
  • Max Welling & Yee Whye Teh (2001)
    Belief Optimization for Binary Networks: A stable Alternative to Loopy Belief Propagation
    UAI2001, Seattle, Washington [ps]
  • Max Welling & Markus Weber(2001) 
    A Constrained EM Algorithm for Independent Component Analysis 
    neural computation 13 (3), pp. 677-689  [ps]
  • Markus Weber, Max Welling & Pietro Perona (2000)
    Unsupervised Learning of Models for Recognition 
    Proc. 6th Europ. Conf. Comp. Vis., ECCV2000, Dublin [ps]
    Winner of the ECCV 2010 Koenderink Prize
  • Markus Weber, Max Welling & Pietro Perona (2000) 
    Towards Automatic Discovery of Object Categories 
    Proc. IEEE Comp. Soc. Conf. Comp. Vis. and Pat. Rec., CVPR20000, Hilton Head Island [ps]
  • Markus Weber, Wolfgang Einhauser, Max Welling & Pietro Perona (2000)
    Viewpoint-Invariant Learning and Detection of Human Heads 
    Proc. 4th Int. Conf. Autom. Face and Gesture Rec., FG2000, Grenoble [ps]
  • Max Welling & Markus Weber (1999) 
    Independent Component Analysis of Incomplete Data 
    Proceedings of the 6th Annual Joint Symposium on Neural Computation, JNSC99, Pasadena [ps]
  • M. Weber, M. Welling & P. Perona (1999)
    Unsupervised learning of models for visual object class recognition
    Proceedings of the 6th Annual Joint Symposium on Neural Computation, JNSC99, Pasadena [ps]

Technical Reports and Research Notes in Machine Learning

  • Max Welling (2008)
    Hard Wall Stochastic Control with Hallucination EM and Power EP
    Technical Report [pdf]
  • Alex Holub, Max Welling and Pietro Perona (2005)
    Exploiting Unlabelled Data for Hybrid Object Classification
    NIPS-2005 workshop in interclass transfer [pdf]
  • Simon Osindero, Max Welling and Geoffrey Hinton and (2004)
    Modeling the Statistics of Natural Images with Topographic Product of Student-t Models
    Technical Report [pdf]
  • Max Welling (2004)
    EM Algorithms for Offset-Normal Shape Densities
    Techical Report [ps]
  • Cristian Sminchisescu, Max Welling and Geoffrey Hinton (2003)
    Generalized Darting Monte Carlo
    Technical Report [pdf]
  • Max Welling (2001)
    Labelling with Loopy Belief Revision
    Research Note [ps]
  • Max Welling & Geoffrey Hinton (2001)
    A New Learning Algorithm for Mean Field Boltzmann Machines
    Technical Report GCNU TR 2001-002 [ps]
  • Yee Whye Teh & Max Welling (2001)
    Passing and Bouncing Messages for Generalized Inference
    Technical Report GCNU TR 2001-001 [ps]
  • Max Welling (1999)
    Robust cumulant expansions for probability density estimation
    Technical Report [ps]