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Prof. dr. M. (Max) Welling

Faculty of Science
Informatics Institute
Visiting address
  • Science Park 904
  • Room number: L4.17
Postal address
  • Postbus 94323
    1090 GH Amsterdam
Contact details
  • Publications

    2022

    2021

    2020

    2019

    2018

    • Cohen, T. S., Geiger, M., Khler, J., & Welling, M. (2018). Spherical CNNs. In International Conference for Learning Representations
    • Federici, M., Ullrich, K., & Welling, M. (2018). Improved Bayesian Compression. In NIPS Workshop NIPS.
    • Hoogeboom, E., Peters, J. W. T., Cohen, T. S., & Welling, M. (2018). HexaConv. In International Conference for Learning Representations
    • Ilse, M., Tomczak, J. M., & Welling, M. (2018). Attention-based Deep Multiple Instance Learning. Proceedings of Machine Learning Research, 80, 2127-2136. http://proceedings.mlr.press/v80/ilse18a.html [details]
    • Kipf, T., Fetaya, E., Wang, K-C., Welling, M., & Zemel, R. (2018). Neural Relational Inference for Interacting Systems. Proceedings of Machine Learning Research, 80, 2688-2697. http://proceedings.mlr.press/v80/kipf18a.html [details]
    • 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). Neural Information Processing Systems. https://papers.nips.cc/paper/2017/file/94b5bde6de888ddf9cde6748ad2523d1-Paper.pdf [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). Neural Information Processing Systems. https://papers.nips.cc/paper/6921-bayesian-compression-for-deep-learning [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). Initialized Equilibrium Propagation for Backprop-Free Training. In International Conference on Machine Learning: Workshop on Credit Assignment in Deep Learning and Deep Reinforcement Learning
    • O'Connor, P. E., Gavves, E., & Welling, M. (2018). Temporally Efficient Deep Learning with Spikes. In International Conference on Learning Representations OpenReview.
    • Oh, C., Gavves, E., & Welling, M. (2018). BOCK: Bayesian Optimization with Cylindrical Kernels. Proceedings of Machine Learning Research, 80, 3868-3877. http://proceedings.mlr.press/v80/oh18a.html [details]
    • Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018). Modeling Relational Data with Graph Convolutional Networks. In A. Gangemi, R. Navigli, M-E. Vidal, P. Hitzler, R. Troncy, L. Hollink, A. Tordai, & M. Alam (Eds.), 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). Springer. https://doi.org/10.1007/978-3-319-93417-4_38 [details]
    • Tomczak, J. M., & Welling, M. (2018). VAE with a VampPrior. Proceedings of Machine Learning Research, 84, 1214-1223. https://arxiv.org/abs/1705.07120 [details]
    • van den Berg, R., Hasenclever, L., Tomczak, J. M., & Welling, M. (2018). Sylvester Normalizing Flows for Variational Inference. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 393-402). Corvallis, Oregon: AUAI Press. [details]

    2017

    2016

    • Chen, Y., & Welling, M. (2016). Herding as a Learning System with Edge-of-Chaos Dynamics. In T. Hazan, G. Papandreou, & D. Tarlow (Eds.), Perturbations, Optimization, and Statistics (pp. 73-125). (Neural Information Processing series). The MIT Press. https://doi.org/10.7551/mitpress/10761.003.0005 [details]
    • 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]
    • Cohen, T. S., & Welling, M. (2016). Group Equivariant Convolutional Networks. JMLR Workshop and Conference Proceedings, 48, 2990-2999. [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). IEEE Computer Society. https://doi.org/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 A. Ihler, & D. Janzing (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Second Conference (2016) : June 25-29, 2016, Jersey City, New Jersey, USA (pp. 192-201). [45] Corvallis, Oregon: AUAI Press. [details]
    • Korattikara, A., Chen, Y., & Welling, M. (2016). Sequential Tests for Large Scale Learning. Neural Computation, 28(1), 45-70. https://doi.org/10.1162/NECO_a_00226 [details]
    • 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., & Welling, M. (2016). Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. JMLR Workshop and Conference Proceedings, 48, 1708-1716. [details]
    • 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]
    • Park, M. J., & Welling, M. (2016). A note on Privacy Preserving Iteratively Reweighted Least Squares. In ICML Workshop on Privacy & Machine Learning https://arxiv.org/abs/1605.07511
    • Park, M., Foulds, J., Chaudhuri, K., & Welling, M. (2016). Private Topic Modeling. In Private Multi-Party Machine Learning: NIPS 2016 workshop : Barcelona, December 9 : PMPML'16 NIPS. [details]
    • Welling, M. (2016). Marrying Graphical Models with Deep Learning. ERCIM News, 107, 20-21. [details]

    2015

    • 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. https://doi.org/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]. https://doi.org/10.1186/s12915-015-0148-y [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 ICLR 2015: accepted papers - Main Conference - Poster Presentations Ithaca, NY: arXiv.org. [details]
    • 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]
    • 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]
    • 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]
    • 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]. https://doi.org/10.1186/s12859-015-0658-1 [details]
    • Meeds, E., Hendriks, R., Al Faraby, S., Bruntink, M., & Welling, M. (2015). MLitB: Machine Learning in the Browser. PeerJ Computer Science, 1, [e11]. https://doi.org/10.7717/peerj-cs.11 [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]
    • 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

    • 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). The MIT Press. https://mitpress.mit.edu/books/advanced-structured-prediction [details]
    • Cohen, T., & Welling, M. (2014). Learning the Irreducible Representations of Commutative Lie Groups. JMLR Workshop and Conference Proceedings, 32, 1755-1763. [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]
    • 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]
    • 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]
    • 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]
    • 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. https://doi.org/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. https://doi.org/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]

    2022

    2021

    2019

    • Winkler, C., Worrall, D., Hoogeboom, E., & Welling, M. (2019). Learning Likelihoods with Conditional Normalizing Flows. In ArXiV arXiv.org.

    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]

    2015

    • Welling, M. (2015). Deep Learning maakt ons bestaan smarter en kwetsbaarder. Het Financieele Dagblad.
    • Welling, M. (2015). Flexibel werken en voor ieder een basisinkomen. Het Financieele Dagblad.

    2014

    • Welling, M. (2014). Big data hoeven privacy niet aan te tasten. Folia.
    • Welling, M. (2014). Hoe ontrafelen we 'wat' en 'waar' in videobeelden? Computable.
    • Welling, M. (2014). When saving all the data captured by the antennas is simply not an option. ASTRON Newsletter.

    2013

    • Welling, M. (2013). Mijn data mogen ze hebben hoor. NRC Opinie.
    • Welling, M. (2013). Open access wel degelijk belangrijk in economie. NRC Opinie.

    2021

    • Keller, T. A., Gao, Q., & Welling, M. (2021). Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders. Paper presented at 3rd Workshop on Shared Visual Representations in Human and Machine Intelligence of the Neural Information Processing Systems conference. https://arxiv.org/abs/2110.13911

    2018

    • Selvan, R., Kipf, T., Welling, M., Pedersen, J. H., Petersen, J., & de Bruijne, M. (2018). Extraction of Airways using Graph Neural Networks. Poster session presented at Medical Imaging with Deep Learning, Abstract Track (MIDL 2018), .

    2017

    • Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Paper presented at 5th International Conference on Learning Representations, Toulon, France.
    • Ullrich, K., Meeds, E. W. F., & Welling, M. (2017). Soft Weight-Sharing for Neural Network Compression. Paper presented at 5th International Conference on Learning Representations, Toulon, France.

    2016

    • Kipf, T. N., & Welling, M. (2016). Variational Graph Auto-Encoders. Paper presented at Bayesian Deep Learning Workshop NIPS 2016, Barcelona, Spain. https://arxiv.org/abs/1611.07308v1
    • 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

    • 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. https://www.deingenieur.nl/artikel/lerende-computerneuronen
    • Welling, M. (10-01-2015). Een computer met een mensenbrein [Print] Parool. Een computer met een mensenbrein.

    2023

    • Hoogeboom, E. (2023). Normalizing flows and diffusion models for discrete and geometric data. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
    • Oh, C. (2023). Bayesian optimization on non-conventional search spaces. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
    • Putzky, P. (2023). Amortized inference in inverse problems. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2022

    • Bongers, S. R. (2022). Causal modeling & dynamical systems: A new perspective on feedback. [details]
    • Hu, S. (2022). Uncertainty, robustness and safety in artificial intelligence, with applications in healthcare. [details]
    • Ilse, M. (2022). Invariance in deep representations. [details]
    • Kool, W. (2022). Learning and optimization in combinatorial spaces: With a focus on deep learning for vehicle routing. [details]
    • Louizos, C. (2022). Probabilistic reasoning for uncertainty & compression in deep learning. [details]
    • Wang, Q. (2022). Functional representation learning for uncertainty quantification and fast skill transfer. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2021

    • Blom, T. (2021). Causality and independence in systems of equations. [details]
    • Cohen, T. S. (2021). Equivariant convolutional networks. [details]
    • Shang, W. (2021). Crafting deep learning models for reinforcement learning and computer vision applications. [details]

    2020

    • Kipf, T. N. (2020). Deep learning with graph-structured representations. [details]
    • O'Connor, P. (2020). Biologically plausible deep learning: Should airplanes flap their wings?. [details]
    • Ullrich, K. (2020). A coding perspective on deep latent variable models. [details]

    2019

    • Satsangi, Y. (2019). Active perception for person tracking. [details]
    • Shiarlis, K. C. (2019). Detaching the strings: Practical algorithms for Learning from Demonstration. [details]

    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.
  • Ancillary activities
    • Canadian Institute for Advanced Research
      Advies, onderzoek
    • Microsoft Research
      Distinguished Scientist, Director Amsterdam MSR Lab
    • Stichting Do & Well
      Goed doel opgericht door mijn vrouw en mijzelf.
    • Scymax
      Beleggen
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