prof. dr. M. (Max) Welling


  • Faculty of Science
    Informatics Institute
  • Visiting address
    Science Park A
    Science Park 904  Room number: C3.259
  • Postal address:
    Postbus  94323
    1090 GH  Amsterdam
  • M.Welling@uva.nl
    T: 0205258256

2018

  • Cohen, T. S., Geiger, M., Khler, J., & Welling, M. (2018). Spherical CNNs. In International Conference for Learning Representations
  • Hoogeboom, E., Peters, J. W. T., Cohen, T. S., & Welling, M. (2018). HexaConv. In International Conference for Learning Representations
  • Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., & Welling, M. (2018). Causal Effect Inference with Deep Latent-Variable Models. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 10, pp. 6447-6457). (Advances in Neural Information Processing Systems; Vol. 30). Red Hook, NY: Curran Associates. [details] 
  • Louizos, C., Ullrich, K., & Welling, M. (2018). Bayesian Compression for Deep Learning. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 5, pp. 3289-3299). (Advances in Neural Information Processing Systems; Vol. 30). Red Hook, NY: Curran Associates. [details] 
  • Louizos, C., Welling, M., & Kingma, D. P. (2018). Learning Sparse Neural Networks through L0 Regularization. In International Conference for Learning Representations
  • O'Connor, P. E., Gavves, E., & Welling, M. (2018). Temporally Efficient Deep Learning with Spikes. In International Conference for Learning Representations
  • Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018). Modeling Relational Data with Graph Convolutional Networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018 : proceedings (pp. 593-607). (Lecture Notes in Computer Science; Vol. 10843). Cham: Springer. DOI: 10.1007/978-3-319-93417-4_38 
  • Tomczak, J. M., & Welling, M. (2018). VAE with a VampPrior. In Proceedings of the International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research; Vol. 84). MIT Press.
  • Federici, M., Ullrich, K., & Welling, M. (2018). Improved Bayesian Compression. In NIPS Workshop

2017

  • Hasenclever, L., Tomczak, J. M., van den Berg, R., & Welling, M. (2017). Variational Inference with Orthogonal Normalizing Flows. In Variational Inference with Orthogonal Normalizing Flows
  • Ilse, M., Tomczak, J. M., & Welling, M. (2017). Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification. In Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification
  • Eck, A., Zintgraf, L. M., de Groot, E. F. J., de Meij, T. G. J., Cohen, T. S., Savelkoul, P. H. M., ... Budding, A. E. (2017). Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. BMC Bioinformatics, 18, [441]. DOI: 10.1186/s12859-017-1843-1  [details] 
  • Eck, A., de Groot, E. F. J., de Meij, T. G. J., Welling, M., Savelkoul, P. H. M., & Budding, A. E. (2017). Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease. Journal of Clinical Microbiology, 55(6), 1720-1732. DOI: 10.1128/JCM.00162-17  [details] 
  • Cohen, T. S., Geiger, M., & Welling, M. (2017). Convolutional Networks for Spherical Signals. In NIPS Workshops
  • Kingma, D., Salimans, T., Josefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2017). Improving Variational Autoencoders with Inverse Autoregressive Flow. In D. D. Lee, U. von Luxburg, R. Garnett, M. Sugiyama, & I. Guyon (Eds.), 30th Annual Conference on Neural Information Processing Systems 2016: Barcelona, Spain, 5-10 December 2016 (Vol. 7, pp. 4743-4751). (Advances in Neural Information Processing Systems; Vol. 29). Red Hook, NY: Curran Associates. [details] 
  • Louizos, C., & Welling, M. (2017). Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. In International Conference on Machine Learning (ICML) 2017 Sydney, Australia: International Conference on Machine Learning (ICML).
  • Park, M. J., Foulds, J., Chaudhuri, K. R., & Welling, M. (2017). Practical Privacy for Expectation Maximization. In Proceedings of the Conference on Artificial Intelligence and Statistics 2017
  • Tomczak, J. M., & Welling, M. (2017). Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow. In Benelearn 2017
  • Adel, T., Cohen, T., Caan, M., Welling, M., AGEhIV Study Group, & Alzheimer's Disease Neuroimaging Initiative (2017). 3D scattering transforms for disease classification in neuroimaging. NeuroImage: Clinical, 14, 506-517. DOI: 10.1016/j.nicl.2017.02.004  [details] 

2016

  • Chen, Y., & Welling, M. (2016). Herding as a Learning System with Edge-of-Chaos Dynamics. In Special Issue on "Perturbations, Optimization and Statistics" Neural Information Processing series.
  • Park, M. J., & Welling, M. (2016). Private Topic Modeling. In Workshop Privacy NIPS 2016
  • Welling, M. (2016). Marrying Graphical Models with deep Learning. ERCIM News, (107).
  • Cohen, T. S., & Welling, M. (2016). Group Equivariant Convolutional Networks. In Group Equivariant Convolutional Networks (Proceedings International Conference Machine Learning (ICML2016)).
  • Li, W., Ahn, S., & Welling, M. (2016). Scalable MCMC for Mixed Membership Stochastic Blockmodels. JMLR Workshop and Conference Proceedings, 51, 723-731. [details] 
  • Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. (2016). The Variational Fair Auto-Encoder. In The Variational Fair Auto-Encoder (Proceedings of the International Conference on Learning Representations (ICLR)).
  • Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. (2016). The Variational Fair Autoencoder. In ICLR 2016: International Conference on Learning Representations: May 2-4, 2016, San Juan, Puerto Rico. Accepted papers (Conference Track) Computational and Biological Learning Society. [details] 
  • Louizos, C., & Welling, M. (2016). Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. In ICML 2016 - Proceedings, 33rd International Conference on Machine Learning, New York, USA
  • Park, M. J., & Welling, M. (2016). A note on Privacy Preserving Iteratively Reweighted Least Squares. In ICML Workshop on Privacy & Machine Learning
  • Chen, Y., Bornn, L., de Freitas, N., Eskelin, M., Fang, J., & Welling, M. (2016). Herded Gibbs Sampling. Journal of Machine Learning Research, 17, [10]. [details] 
  • El-Helw, I., Hofman, R., Li, W., Ahn, S., Welling, M., & Bal, H. (2016). Scalable Overlapping Community Detection. In 2016 IEEE 30th International Parallel and Distributed Processing Symposium Workshops : IPDPSW 2016: proceedings : 23-27 May 2016, Chicago, Illinois (pp. 1463-1472). Los Alamitos, California: IEEE Computer Society. DOI: 10.1109/IPDPSW.2016.165  [details] 
  • Foulds, J., Geumlek, J., Welling, M., & Chaudhuri, K. R. (2016). On the Theory and Practice of Privacy Preserving Data Analysis. In On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis (Proceedings of the Conference on Uncertainty in Artificial Intelligence).
  • Korattikara, A., Chen, Y., & Welling, M. (2016). Sequential Tests for Large Scale Learning. Neural Computation, 28(1), 45-70. DOI: 10.1162/NECO_a_00226  [details] 

2015

  • Meeds, E., Hendriks, R., Al Faraby, S., Bruntink, M., & Welling, M. (2015). MLitB: Machine Learning in the Browser. PeerJ Computer Science, 1, [e11]. DOI: 10.7717/peerj-cs.11  [details] 
  • Cohen, T. S., & Welling, M. (2015). Harmonic Exponential Families on Manifolds. JMLR Workshop and Conference Proceedings, 37, 1757-1765. [details] 
  • Cohen, T. S., & Welling, M. (2015). Transformation Properties of Learned Visual Representations. In International Conference on Learning Representations (ICLR)
  • Kingma, D. P., Rezende, D. J., Mohamed, S., & Welling, M. (2015). Semi-supervised Learning with Deep Generative Models. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), 28th Annual Conference on Neural Information Processing Systems 2014: December 8-13, 2014, Montreal, Canada (Vol. 4, pp. 3581-3589). (Advances in Neural Information Processing Systems; Vol. 27). Red Hook, NY: Curran. [details] 
  • Kingma, D. P., Salimans, T., & Welling, M. (2015). Variational Dropout and the Local Reparameterization Trick. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), 29th Annual Conference on Neural Information Processing Systems 2015: Montreal, Canada, 7-12 December 2015 (Vol. 3, pp. 2575-2583). (Advances in Neural Information Processing Systems; Vol. 28). Red Hook, NY: Curran Associates. [details] 
  • Meeds, E., Chiang, M., Lee, M., Cinquin, O., Lowengrub, J., & Welling, M. (2015). POPE: Post Optimization Posterior Evaluation of Likelihood Free Models. BMC Bioinformatics, 16, [264]. DOI: 10.1186/s12859-015-0658-1  [details] 
  • Meeds, E., Leenders, R., & Welling, M. (2015). Hamiltonian ABC. In M. Meila, & T. Heskes (Eds.), Uncertainty in Artificial Intelligence: proceedings of the thirty-first conference (2015): July 12-16, Amsterdam, Netherlands (pp. 582-591). Corvallis, OR: AUAI Press. [details] 
  • Meeds, E., & Welling, M. (2015). Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), 29th Annual Conference on Neural Information Processing Systems 2015: Montreal, Canada, 7-12 December 2015 (Vol. 3, pp. 2080-2088). (Advances in Neural Information Processing Systems; Vol. 28). Red Hook, NY: Curran Associates. [details] 
  • Ahn, S., Korattikara, A., Liu, N., Rajan, S., & Welling, M. (2015). Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC. In KDD'15: proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: August 10-13, 2015, Sydney, Australia (pp. 9-18). New York, NY: Association for Computing Machinery. DOI: 10.1145/2783258.2783373  [details] 
  • Chiang, M., Cinquin, A., Paz, A., Meeds, E., Price, C. A., Welling, M., & Cinquin, O. (2015). Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation. BMC Biology, 13, [51]. DOI: 10.1186/s12915-015-0148-y  [details] 
  • Korattikara, A., Rathod, V., Murphy, K., & Welling, M. (2015). Bayesian Dark Knowledge. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), 29th Annual Conference on Neural Information Processing Systems 2015: Montreal, Canada, 7-12 December 2015 (Vol. 4, pp. 3438-3446). (Advances in Neural Information Processing Systems; Vol. 28). Red Hook, NY: Curran Associates. [details] 
  • Salimans, T., Kingma, D. P., & Welling, M. (2015). Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. JMLR Workshop and Conference Proceedings, 37, 1218-1226. [details] 

2014

  • Cohen, T., & Welling, M. (2014). Learning the Irreducible Representations of Commutative Lie Groups. JMLR Workshop and Conference Proceedings, 32, 1755-1763. [details] 
  • Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Conference proceedings: papers accepted to the International Conference on Learning Representations (ICLR) 2014 Ithaca, NY: arXiv.org. [details] 
  • Kingma, D. P., & Welling, M. (2014). Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. JMLR Workshop and Conference Proceedings, 32, 1782-1790. [details] 
  • Meeds, E., & Welling, M. (2014). GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. In N. Zhang, & J. Tian (Eds.), Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence: Quebec City, Quebec, Canada: July 23-27, 2014: UAI2014 (pp. 593-602). Corvallis, Oregon: AUAI Press. [details] 
  • Ahn, S., Shahbaba, B., & Welling, M. (2014). Distributed Stochastic Gradient MCMC. JMLR Workshop and Conference Proceedings, 32, 1044-1052. [details] 
  • Chen, Y., Gelfand, A. E., & Welling, M. (2014). Herding for Structured Prediction. In S. Nowozin, P. V. Gehler, J. Jancsary, & C. H. Lampert (Eds.), Advanced structured prediction (pp. 187-212). (Neural information processing series). Cambridge, MA: The MIT press. [details] 
  • DuBois, C., Korattikara, A., Welling, M., & Smyth, P. (2014). Approximate Slice Sampling for Bayesian Posterior Inference. JMLR Workshop and Conference Proceedings, 33, 185-193. [details] 
  • Korattikara, A., Chen, Y., & Welling, M. (2014). Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. JMLR Workshop and Conference Proceedings, 32, 181-189. [details] 
  • Salimans, T., Kingma, D. P., & Welling, M. (2014). Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. In Accepted papers: Advances in Variational Inference: NIPS 2014 Workshop: 13 December 2014, Convention and Exhibition Center, Montreal, Canada NIPS Foundation. [details] 

2013

  • Ahn, S., Chen, Y., & Welling, M. (2013). Distributed and Adaptive Darting Monte Carlo through Regenerations. JMLR Workshop and Conference Proceedings, 31, 108-116. [details] 
  • Bornn, L., Chen, Y., de Freitas, N., Eskelin, M., Fang, J., & Welling, M. (2013). Herded Gibbs Sampling. In International Conference on Learning Representation 2013 Ithaca, NY: arXiv.org. [details] 
  • Boyles, L., & Welling, M. (2013). The time-marginalized coalescent prior for hierarchical clustering. In P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Eds.), 26th Annual Conference on Neural Information Processing Systems 2012: December 3-6, 2012, Lake Tahoe, Nevada, USA (Vol. 4, pp. 2969-2977). (Advances in Neural Information Processing Systems; Vol. 25). Red Hook, NY: Curran Associates. [details] 
  • Chen, Y., & Welling, M. (2013). Evidence Estimation for Bayesian Partially Observed MRFs. JMLR Workshop and Conference Proceedings, 31, 178-186. [details] 
  • Foulds, J., Boyles, L., DuBois, C., Smyth, P., & Welling, M. (2013). Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation. In I. S. Dhillon, Y. Koren, R. Ghani, T. E. Senator, P. Bradley, R. Parekh, J. He, R. L. Grossman, ... R. Uthurusamy (Eds.), KDD '13: the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: August 11-14, 2013, Chicago, Illinois, USA (pp. 446-454). New York: ACM. DOI: 10.1145/2487575.2487697  [details] 
  • Korattikara, A., Chen, Y., & Welling, M. (2013). Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. In 2013 JSM proceedings: papers presented at the Joint Statistical Meetings, Montréal, Québec, Canada, August 3-8, 2013, and other ASA-sponsored conferences [cd-rom] (pp. 236-250). Alexandria, Virginia: American Statistical Association. [details] 
  • Welinder, P., Welling, M., & Perona, P. (2013). A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration. In Proceedings: 2013 IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2013: 23-28 June 2013, Portland, Oregon, USA (pp. 3262-3269). Los Alamitos, CA: IEEE Computer Society Conference Publishing Services. DOI: 10.1109/CVPR.2013.419  [details] 

2012

  • Ahn, S., Korattikara, A., & Welling, M. (2012). Bayesian posterior sampling via stochastic gradient Fisher scoring. In J. Langford, & J. Pineau (Eds.), Proceedings of Twenty-Ninth International Conference Machine Learning. - Vol. 2 (pp. 1591-1598). Madison, WI: International Machine Learning Society. [details] 
  • Chen, Y., & Welling, M. (2012). Bayesian structure learning for Markov Random Fields with a spike and slab prior. In N. de Freitas, & K. Murphy (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Twenty-Eight conference (2012): August 15-17, 2012, Catalina Island, CA (pp. 174-184). Corvallis, OR: AUAI Press. [details] 
  • Gelfand, A. E., & Welling, M. (2012). Generalized belief propagation on tree robust structured region graphs. In K. Murphy, & N. de Freitas (Eds.), Uncertainty in Artificial: proceedings of the Twenty-Eight conference (2012): August 15-17, 2012 Catalina Island, CA (pp. 296-305). Corvallis, OR: AUAI Press. [details] 

2016

  • Tomczak, J. M., & Welling, M. (2016). Improving Variational Auto-Encoders using Householder Flow. Paper presented at Bayesian Deep Learning Workshop NIPS 2016, Barcelona, Spain. [details] 

2014

  • Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z., & Weinberger, K. Q. (2014). 27th Annual Conference on Neural Information Processing Systems 2013: December 5-10, Lake Tahoe, Nevada, USA. (Advances in Neural Information Processing Systems; Vol. 26). Red Hook, NY: Curran. [details] 

2013

  • Ullrich, K., Meeds, E. W. F., & Welling, M. (2013). Soft Weight-Sharing for Neural Network Compression. 1-16. Paper presented at International Conference on Learning Representations (ICLR), .

2017

  • van der Wel, E., Ullrich, K., & Welling, M. (2017). Optical Music Recognition with Convolutional Sequence-to-Sequence Models. Paper presented at International Society for Music Information Retrieval Conference , Suzhou, China.

2014

  • Welling, M. (2014). Exploiting the Statistics of Learning and Inference. Paper presented at NIPS 2014 Workshop on "Probabilistic Models for Big Data", .

2013

  • Meeds, E., & Welling, M. (2013). Inference in Stochastic Biological Systems using Gaussian Process Surrogate ABC. Poster session presented at 2013 NIPS Workshop on Machine Learning in Computational Biology, Lake Tahoe, NV, .

Media appearance

  • Welling, M. (20-10-2016). Contribution to magazine ICT & Health. Een pitbull waakt voor het laaghangend fruit.
  • Welling, M. (01-09-2016). Column FD. Monthly Column in Financieel Dagblad.
  • Welling, M. (31-05-2016). Interview BNR Radio. Interview BNR Radio.
  • Welling, M. (30-04-2016). En toen ging de computer zelf leren” (door Bennie Mols). Interview NRC.
  • Welling, M. (23-01-2015). Lerende computer-neuronen [Print] De Ingenieur. Lerende computer-neuronen.
  • Welling, M. (10-01-2015). Een computer met een mensenbrein [Print] Parool. Een computer met een mensenbrein.

2017

  • Kingma, D. P. (2017). Variational inference & deep learning: A new synthesis [details] 

2016

  • O'Connor, P., & Welling, M. (2016). Deep Spiking Networks. Ithaca, NY: arXiv.org. [details] 
This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library  or the Pure staff  of your faculty / institute. Log in to Pure  to edit your publications. Log in to Personal Page Publication Selection tool  to manage the visibility of your publications on this list.
  • Irvine University (US)
    hoogleraar
  • Stratified Medical
    Adviseur
  • Scyfer B.V.
    Onbezoldigd advies
  • various companies & organizations
    Presentations
  • Qualcomm NL
    employment 2 days a week

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