Header logo is ei

Thumb sm isaweb
Isabel Valera (Project leader)
Max Planck Research Group Leader
ei Thumb sm profile
Adam Scibior
Alumni
ei Thumb sm 2016 07
Matthias Bauer
Ph.D. Student
Thumb sm matejbalog13
Matej Balog
Ph.D. Student
14 results

2018


no image
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

Ruiz, F. J. R., Valera, I., Svensson, L., Perez-Cruz, F.

IEEE Transactions on Cognitive Communications and Networking, 4(2):177-191, June 2018 (article)

DOI Project Page [BibTex]

2018

DOI Project Page [BibTex]


no image
Learning Invariances using the Marginal Likelihood

van der Wilk, M., Bauer, M., John, S. T., Hensman, J.

Advances in Neural Information Processing Systems 31, pages: 9960-9970, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Boosting Black Box Variational Inference

Locatello*, F., Dresdner*, G., R., K., Valera, I., Rätsch, G.

Advances in Neural Information Processing Systems 31, pages: 3405-3415, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [BibTex]


no image
Functional Programming for Modular Bayesian Inference

Ścibior, A., Kammar, O., Ghahramani, Z.

Proceedings of the ACM on Functional Programming (ICFP), 2(Article No. 83):1-29, ACM, 2018 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Denotational Validation of Higher-order Bayesian Inference

Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., Ostermann, K., Moss, S. K., Heunen, C., Ghahramani, Z.

Proceedings of the ACM on Principles of Programming Languages (POPL), 2(Article No. 60):1-29, ACM, 2018 (conference)

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Boosting Variational Inference: an Optimization Perspective

Locatello, F., Khanna, R., Ghosh, J., Rätsch, G.

Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84, pages: 464-472, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)

link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]

2017


no image
Lost Relatives of the Gumbel Trick

Balog, M., Tripuraneni, N., Ghahramani, Z., Weller, A.

Proceedings of the 34th International Conference on Machine Learning, 70, pages: 371-379, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (conference)

Code link (url) Project Page [BibTex]

2017

Code link (url) Project Page [BibTex]

2016


no image
Fabular: Regression Formulas As Probabilistic Programming

Borgström, J., Gordon, A. D., Ouyang, L., Russo, C., Ścibior, A., Szymczak, M.

Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL), pages: 271-283, POPL ’16, ACM, January 2016 (conference)

DOI Project Page [BibTex]

2016

DOI Project Page [BibTex]


no image
Understanding Probabilistic Sparse Gaussian Process Approximations

Bauer, M., van der Wilk, M., Rasmussen, C. E.

Advances in Neural Information Processing Systems 29, pages: 1533-1541, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
The Mondrian Kernel

Balog, M., Lakshminarayanan, B., Ghahramani, Z., Roy, D. M., Teh, Y. W.

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), (Editors: Ihler, Alexander T. and Janzing, Dominik), June 2016 (conference)

Arxiv link (url) Project Page [BibTex]

Arxiv link (url) Project Page [BibTex]


no image
Consistent Kernel Mean Estimation for Functions of Random Variables

Simon-Gabriel*, C. J., Ścibior*, A., Tolstikhin, I., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1732-1740, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016, *joint first authors (conference)

link (url) Project Page Project Page Project Page [BibTex]

link (url) Project Page Project Page Project Page [BibTex]


no image
Batch Bayesian Optimization via Local Penalization

González, J., Dai, Z., Hennig, P., Lawrence, N.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 51, pages: 648-657, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C.), May 2016 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Thumb xl screen shot 2018 10 09 at 11.42.49
Active Uncertainty Calibration in Bayesian ODE Solvers

Kersting, H., Hennig, P.

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), pages: 309-318, (Editors: Ihler, A. and Janzing, D.), AUAI Press, June 2016 (conference)

Abstract
There is resurging interest, in statistics and machine learning, in solvers for ordinary differential equations (ODEs) that return probability measures instead of point estimates. Recently, Conrad et al.~introduced a sampling-based class of methods that are `well-calibrated' in a specific sense. But the computational cost of these methods is significantly above that of classic methods. On the other hand, Schober et al.~pointed out a precise connection between classic Runge-Kutta ODE solvers and Gaussian filters, which gives only a rough probabilistic calibration, but at negligible cost overhead. By formulating the solution of ODEs as approximate inference in linear Gaussian SDEs, we investigate a range of probabilistic ODE solvers, that bridge the trade-off between computational cost and probabilistic calibration, and identify the inaccurate gradient measurement as the crucial source of uncertainty. We propose the novel filtering-based method Bayesian Quadrature filtering (BQF) which uses Bayesian quadrature to actively learn the imprecision in the gradient measurement by collecting multiple gradient evaluations.

link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]


Thumb xl untitled
Probabilistic Approximate Least-Squares

Bartels, S., Hennig, P.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 51, pages: 676-684, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C. ), May 2016 (conference)

Abstract
Least-squares and kernel-ridge / Gaussian process regression are among the foundational algorithms of statistics and machine learning. Famously, the worst-case cost of exact nonparametric regression grows cubically with the data-set size; but a growing number of approximations have been developed that estimate good solutions at lower cost. These algorithms typically return point estimators, without measures of uncertainty. Leveraging recent results casting elementary linear algebra operations as probabilistic inference, we propose a new approximate method for nonparametric least-squares that affords a probabilistic uncertainty estimate over the error between the approximate and exact least-squares solution (this is not the same as the posterior variance of the associated Gaussian process regressor). This allows estimating the error of the least-squares solution on a subset of the data relative to the full-data solution. The uncertainty can be used to control the computational effort invested in the approximation. Our algorithm has linear cost in the data-set size, and a simple formal form, so that it can be implemented with a few lines of code in programming languages with linear algebra functionality.

link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]