Nieuws & Agenda

dhr. prof. dr. M. (Max) Welling


  • Faculteit der Natuurwetenschappen, Wiskunde en Informatica
    IVI
  • Bezoekadres
    Science Park A
    Science Park 904  Amsterdam
    Kamernummer: C3.259
  • Postadres:
    Postbus  94323
    1090 GH  Amsterdam
  • M.Welling@uva.nl
    T: 0205258256

2017

  • Kingma, D. P., Salimans, T., Josefowicz, R., Chen, X., Sutskever, I., & Welling, M. (in press). Improving Variational Inference with Inverse Autoregressive Flow, In Proceedings of the Conference on Neural Information Processing Systems (NIPS2016).
  • 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

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.
  • 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.
  • Park, M. J., & Welling, M. (2016). Private Topic Modeling. In Workshop Privacy NIPS 2016
  • Tomczak, J. M., & Welling, M. (2016). Improving Variational Auto-Encoders using Householder Flow. In Bayesian Deep Learning Workshop NIPS 2016
  • 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 Proceedings of the 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics.
  • 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

  • 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., Salimans, T., & Welling, M. (2015). Variational Dropout and the Local Reparameterization Trick. Advances in Neural Information Processing Systems, 28, 2575-2583. [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., Hendriks, R., Faraby, S., Bruntink, M., & Welling, M. (2015). MLitB: Machine Learning in the Browser. PeerJ Computer Science.
  • 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. Advances in Neural Information Processing Systems, 28, 2080-2088. [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. Advances in Neural Information Processing Systems, 28, 3438-3446. [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., Rezende, D. J., Mohamed, S., & Welling, M. (2014). Semi-supervised Learning with Deep Generative Models. Advances in Neural Information Processing Systems, 27, 3581-3589. [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] 
  • 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] 
  • Boyles, L., & Welling, M. (2012). The time-marginalized coalescent prior for hierarchical clustering. Advances in Neural Information Processing Systems, 25, 2969-2977. [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] 

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, .

Mediaoptreden

  • Welling, Max (20-10-2016): Contribution to magazine ICT & Health. Een pitbull waakt voor het laaghangend fruit.
  • Welling, Max (01-09-2016): Column FD. Monthly Column in Financieel Dagblad.
  • Welling, Max (31-05-2016): Interview BNR Radio. Interview BNR Radio.
  • Welling, Max (30-04-2016): En toen ging de computer zelf leren” (door Bennie Mols). Interview NRC.
  • Welling, Max (23-01-2015): Lerende computer-neuronen [Print] De Ingenieur. Lerende computer-neuronen.
  • Welling, Max (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.
  • Irvine University (US)
    hoogleraar
  • Stratified Medical
    Adviseur
  • Scyfer B.V.
    Onbezoldigd advies

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