Working Papers
M.Park, J.Foulds, K.Chaudhuri, M.Welling
Practical Privacy for Expectation Maximization,
2016, Under Review [pdf]
M.Park, J.Foulds, K.Chaudhuri, M.Welling
Variational Bayes in Private Settings (VIPS),
2016, Under Review [pdf]
T.Cohen, M.Welling,
Steerable CNNs,
2016, Under Review [pdf]
G.Bertone, M.P.Deisenroth, J.S.Kim, S.Liem, R.Ruiz de Austri, M.Welling
Accelerating the BSM interpretation of LHC data with machine learning,
2016, Under Review [pdf]
P.O’Connor, M.Welling,
Sigma Delta Quantized Networks,
2016, Under Review [pdf]
P.O’Connor, M.Welling,
Deep Spiking Networks,
2016, Under Review [pdf]
T.Kipf, M.Welling
Semisupervised Classification with Graph Convolutional Networks,
2016, Under Review [pdf]
A. Moreno, T.Adel, T.Meeds, J.M.Rehg, M.Welling
Automatic Variational ABC,
2016, Under Review [pdf]
L.Zintgraf, T.Cohen, T.Adel, M.Welling,
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis,
2016, Under Review [pdf]
K.Ullrich, T.Meeds, M.Welling,
Soft Weight-sharing For Neural Network Compression,
2016, Under Review [pdf]
P.Putzky, M.Welling,
Recurrent Inference Machines for Solving Inverse Problems,
2016, Under Review [pdf]
Published Papers
J.M.Tomczak, M.Welling
Improving Variational Auto-Encoders using Householder Flow
NIPS Workshops 2016 [pdf]
T. Kipf, M.Welling
Variational Graph Auto-Encoders,
NIPS Workshops 2016 [pdf]
M. Park, J. Foulds, K. Chaudhuri, M.Welling
Private Topic Modeling,
NIPS Workshops 2016 [pdf]
D. Kingma, T. Salimans, R. Josefowicz, X. Chen, I. Sutskever, M.Welling
Improving Variational Inference with Inverse Autoregressive Flow,
NIPS 2016 [pdf]
M. Park, M.Welling
A note on Privacy Preseving Iteratively Reweighted Least Squares,
ICML Workshops 2016 [pdf]
NRC Live Opinie on Healthcare
T. Cohen, M.Welling,
Group Equivariant Convolutional Networks,
ICML 2016 [pdf, suppl.mat., github-experiments, github-software]
C. Louizos, M. Welling,
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors,
ICML2016 [pdf]
Interview NRC Bennie Mols
En Toen Ging de Computer Zelf Leren [pdf]
I. El-Helw, R. Hofman, W. Li, S. Ahn, M. Welling, H. Bal
Scalable Overlapping Community Detection
ParLearning 2016 [pdf] BEST PAPER AWARD
J. Foulds, J. Geumlek, M. Welling, K. Chaudhuri
On the Theory and Practice of Privacy Preserving Data Analysis
UAI 2016 [pdf, suppl.mat.]
Y. Chen, M. Welling
Herding as a Learning System with Edge-of-Chaos Dynamics
Special Issue on “Perturbations, Optimization and Statistics”
Eds. T. Hazan, G. Papandreou, D. Tarlow; 2016 [pdf]
M. Welling
Are Machine Learning and Statistics Complementary?
Contribution to the Roundtable Discussion at the 6th IMSISBA meeting on “Data Science in the next 50 years”. 2015 [pdf]
W. Li, S. Ahn, M. Welling
Scalable Markov Chain Monte Carlo for Bayesian Network Models submitted, AISTATS 2015 [pdf]
C. Louizos, K. Swersky, Y. Li, M. Welling, R. Zemel
Deep Generative Models for Invariant Representations, ICLR 2016 [pdf]
M. Welling
Deep Learning maakt ons leven gemakkelijker – maar ook kwetsbaarder Financieel Dagblad, 26/9/2015 [pdf]
A. Korattikara, Y. Chen, M. Welling
Sequential Tests for Large Scale Learning, Neural Computation (accepted) 2015 [pdf]
T. Meeds, M. Welling
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference NIPS 2015 [pdf]
D. Kingma, T. Salimans, M. Welling
Variational Dropout and the Local Reparameterization Trick NIPS 2015 [pdf]
A. Korattikara, V. Rathod, K.Murphy, M. Welling
Bayesian Dark Knowledge NIPS 2015 [pdf]
M. Welling
Flexible Werken en voor Ieder een Basisinkomen Financieel Dagblad, 22/8/2015 [pdf]
T. Meeds, R. Hendriks, S. al Faraby, M. Bruntink, M. Welling
MLitB: Machine Learning in the Browser PeerJ Computer Science; 2015 [pdf]
T. Meeds, M. Chiang, M. Lee, O. Cinquin, J. Lowengrub, M. Welling
POPE: Post Optimization Posterior Evaluation of Likelihood Free Models BMC Bioinformatics; 2015 (16:264)[pdf]
M. Chiang, A. Cinquin, A.Paz, E. Meeds, M.Welling, O. Cinquin
Control of C.Elegans Stem Cell Cycling Speed Meets Requirements of Design to Minimize Mutation Accumulation BMC Bioinformatics; 2015 (13:51)[pdf]
M.Welling, Y.W. Teh, C. Andrieu, J. Kominiarczuk, T. Meeds
Bayesian Inference and Big Data: A Snapshot from a Conference The ISBA Bulletin, V.21, Nr.4, Dec. 2015 [pdf]
T. S. Ahn, A. Korattikara, N. Liu, S. Rajan, M. Welling
Large Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC KDD 2015 [pdf]
Meeds, R. Leenders, M. Welling
Hamiltonian ABC UAI 2015 [pdf]
T. Cohen, M. Welling
Harmonic Exponential Families on Manifolds ICML 2015, [pdf, suppl. mat.]
T. Salimans, D. Kingma, M. Welling
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap ICML 2015 [pdf]
T. Cohen, M. Welling
Tranformation Properties of Learned Visual Representations ICLR 2015 [pdf]
M. Welling and Y.W. Teh
Bayesian Inference with Big Data: A Snapshot from a Conference BIBiD 2014 [pdf]
D. Kingma, S. Mohamed, D. Rezende and M. Welling(2014)
Semi-supervised Learning with Deep Generative Models NIPS 2014 [arXiv, software]
T. Meeds and M. Welling(2014)
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation UAI 2014 [pdf]
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], JMLR [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 ICML2012 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 ScholarPedia2007 [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 ICANN2007[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 Vicenc Gomez]
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 ECCV2010 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]