Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

386 results (View BibTeX file of all listed publications)

2019

Selecting causal brain features with a single conditional independence test per feature

Mastakouri, A., Schölkopf, B., Janzing, D.

Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference) Accepted

[BibTex]

2019

Mastakouri, A., Schölkopf, B., Janzing, D. Selecting causal brain features with a single conditional independence test per feature Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference) Accepted

[BibTex]

Neural Signatures of Motor Skill in the Resting Brain

Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M.

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), October 2019 (conference) Accepted

[BibTex]

Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M. Neural Signatures of Motor Skill in the Resting Brain Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), October 2019 (conference) Accepted

[BibTex]

Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance

Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M.

Engineering in Medicine and Biology Conference (EMBC), July 2019 (conference) Accepted

arXiv PDF [BibTex]

Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M. Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance Engineering in Medicine and Biology Conference (EMBC), July 2019 (conference) Accepted

arXiv PDF [BibTex]

Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 49, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

link (url) [BibTex]

Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., Schölkopf, B. Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 49, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

link (url) [BibTex]

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 213, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

link (url) [BibTex]

Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R. The Sensitivity of Counterfactual Fairness to Unmeasured Confounding Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 213, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

link (url) [BibTex]

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 53, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019, *equal contribution (conference)

link (url) [BibTex]

Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B. The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 53, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019, *equal contribution (conference)

link (url) [BibTex]

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., Kersting, K., Ghahramani, Z.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 124, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

link (url) [BibTex]

Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., Kersting, K., Ghahramani, Z. Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 124, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

link (url) [BibTex]

Kernel Mean Matching for Content Addressability of GANs

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 3140-3151, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019, *equal contribution (conference)

PDF link (url) [BibTex]

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B. Kernel Mean Matching for Content Addressability of GANs Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 3140-3151, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019, *equal contribution (conference)

PDF link (url) [BibTex]

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., Bachem, O.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

PDF link (url) [BibTex]

Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., Bachem, O. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

PDF link (url) [BibTex]

Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

Abstract

Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.

Code video demo pdf link (url) [BibTex]

Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H. Local Temporal Bilinear Pooling for Fine-grained Action Parsing In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

Code video demo pdf link (url) [BibTex]

Generate Semantically Similar Images with Kernel Mean Matching

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

6th Workshop Women in Computer Vision (WiCV) (oral presentation), June 2019, *equal contribution (conference) Accepted

[BibTex]

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B. Generate Semantically Similar Images with Kernel Mean Matching 6th Workshop Women in Computer Vision (WiCV) (oral presentation), June 2019, *equal contribution (conference) Accepted

[BibTex]

Projections for Approximate Policy Iteration Algorithms

Akrour, R., Pajarinen, J., Peters, J., Neumann, G.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 181-190, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

link (url) [BibTex]

Akrour, R., Pajarinen, J., Peters, J., Neumann, G. Projections for Approximate Policy Iteration Algorithms Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 181-190, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

link (url) [BibTex]

Switching Linear Dynamics for Variational Bayes Filtering

Becker-Ehmck, P., Peters, J., van der Smagt, P.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 553-562, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

link (url) [BibTex]

Becker-Ehmck, P., Peters, J., van der Smagt, P. Switching Linear Dynamics for Variational Bayes Filtering Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 553-562, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

link (url) [BibTex]

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

Suter, R., Miladinovic, D., Schölkopf, B., Bauer, S.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 6056-6065, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

PDF link (url) [BibTex]

Suter, R., Miladinovic, D., Schölkopf, B., Bauer, S. Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 6056-6065, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

PDF link (url) [BibTex]

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Simon-Gabriel, C., Ollivier, Y., Bottou, L., Schölkopf, B., Lopez-Paz, D.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 5809-5817, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

PDF link (url) [BibTex]

Simon-Gabriel, C., Ollivier, Y., Bottou, L., Schölkopf, B., Lopez-Paz, D. First-Order Adversarial Vulnerability of Neural Networks and Input Dimension Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 5809-5817, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

PDF link (url) [BibTex]

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Ialongo, A. D., Van Der Wilk, M., Hensman, J., Rasmussen, C. E.

In Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 2931-2940, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (inproceedings)

PDF link (url) [BibTex]

Ialongo, A. D., Van Der Wilk, M., Hensman, J., Rasmussen, C. E. Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models In Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 2931-2940, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (inproceedings)

PDF link (url) [BibTex]

Meta learning variational inference for prediction

Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

arXiv link (url) [BibTex]

Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R. Meta learning variational inference for prediction 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

arXiv link (url) [BibTex]

Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Lutter, M., Ritter, C., Peters, J.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

Lutter, M., Ritter, C., Peters, J. Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

DeepOBS: A Deep Learning Optimizer Benchmark Suite

Schneider, F., Balles, L., Hennig, P.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

Schneider, F., Balles, L., Hennig, P. DeepOBS: A Deep Learning Optimizer Benchmark Suite 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

Disentangled State Space Models: Unsupervised Learning of Dynamics across Heterogeneous Environments

Miladinović*, D., Gondal*, M. W., Schölkopf, B., Buhmann, J. M., Bauer, S.

Deep Generative Models for Highly Structured Data Workshop at ICLR, May 2019, *equal contribution (conference)

link (url) [BibTex]

Miladinović*, D., Gondal*, M. W., Schölkopf, B., Buhmann, J. M., Bauer, S. Disentangled State Space Models: Unsupervised Learning of Dynamics across Heterogeneous Environments Deep Generative Models for Highly Structured Data Workshop at ICLR, May 2019, *equal contribution (conference)

link (url) [BibTex]

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., Rätsch, G.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., Rätsch, G. SOM-VAE: Interpretable Discrete Representation Learning on Time Series 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

link (url) [BibTex]

Resampled Priors for Variational Autoencoders

Bauer, M., Mnih, A.

22nd International Conference on Artificial Intelligence and Statistics, April 2019 (conference) Accepted

arXiv [BibTex]

Bauer, M., Mnih, A. Resampled Priors for Variational Autoencoders 22nd International Conference on Artificial Intelligence and Statistics, April 2019 (conference) Accepted

arXiv [BibTex]

Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features

von Kügelgen, J., Mey, A., Loog, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1361-1369, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF link (url) [BibTex]

von Kügelgen, J., Mey, A., Loog, M. Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1361-1369, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF link (url) [BibTex]

Sobolev Descent

Mroueh, Y., Sercu, T., Raj, A.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 2976-2985, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF link (url) [BibTex]

Mroueh, Y., Sercu, T., Raj, A. Sobolev Descent Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 2976-2985, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF link (url) [BibTex]

Fast and Robust Shortest Paths on Manifolds Learned from Data

Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF link (url) [BibTex]

Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M. Fast and Robust Shortest Paths on Manifolds Learned from Data Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF link (url) [BibTex]

Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

de Roos, F., Hennig, P.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

Abstract

Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also empirically show to work well.

PDF link (url) [BibTex]

de Roos, F., Hennig, P. Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF link (url) [BibTex]

Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs

Wenk, P., Gotovos, A., Bauer, S., Gorbach, N., Krause, A., Buhmann, J. M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1351-1360, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF PDF link (url) [BibTex]

Wenk, P., Gotovos, A., Bauer, S., Gorbach, N., Krause, A., Buhmann, J. M. Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1351-1360, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

PDF PDF link (url) [BibTex]

Learning Transferable Representations

Rojas-Carulla, M.

University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Rojas-Carulla, M. Learning Transferable Representations University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Sample-efficient deep reinforcement learning for continuous control

Gu, S.

University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Gu, S. Sample-efficient deep reinforcement learning for continuous control University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Formally justified and modular Bayesian inference for probabilistic programs

Ścibior*, A.

University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Ścibior*, A. Formally justified and modular Bayesian inference for probabilistic programs University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

Mehrjou, A., Jitkrittum, W., Schölkopf, B., Muandet, K.

2019 (conference) Submitted

arXiv [BibTex]

Mehrjou, A., Jitkrittum, W., Schölkopf, B., Muandet, K. Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces 2019 (conference) Submitted

arXiv [BibTex]

Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Xu, J.

Technical University of Munich, Germany, 2019 (mastersthesis)

[BibTex]

Xu, J. Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing Technical University of Munich, Germany, 2019 (mastersthesis)

[BibTex]

AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

Abbati*, G., Wenk*, P., Osborne, M. A., Krause, A., Schölkopf, B., Bauer, S.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, 2019, *equal contribution (conference)

PDF link (url) [BibTex]

Abbati*, G., Wenk*, P., Osborne, M. A., Krause, A., Schölkopf, B., Bauer, S. AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, 2019, *equal contribution (conference)

PDF link (url) [BibTex]

Pragmatism and Variable Transformations in Causal Modelling

Weichwald, S.

ETH Zurich, 2019 (phdthesis)

[BibTex]

Weichwald, S. Pragmatism and Variable Transformations in Causal Modelling ETH Zurich, 2019 (phdthesis)

[BibTex]

Kernel Stein Tests for Multiple Model Comparison

Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W.

Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

[BibTex]

Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W. Kernel Stein Tests for Multiple Model Comparison Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

[BibTex]

Quantification of tumor heterogeneity using PET/MRI and machine learning

Katiyar, P.

Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

[BibTex]

Katiyar, P. Quantification of tumor heterogeneity using PET/MRI and machine learning Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

[BibTex]

MYND: A Platform for Large-scale Neuroscientific Studies

Hohmann, M. R., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI), 2019 (conference) Accepted

[BibTex]

Hohmann, M. R., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B. MYND: A Platform for Large-scale Neuroscientific Studies Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI), 2019 (conference) Accepted

[BibTex]

A Kernel Stein Test for Comparing Latent Variable Models

Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A.

2019 (conference) Submitted

arXiv [BibTex]

Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A. A Kernel Stein Test for Comparing Latent Variable Models 2019 (conference) Submitted

arXiv [BibTex]

Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning

Bauer, M.

University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

Bauer, M. Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning University of Cambridge, UK, 2019 (phdthesis)

[BibTex]

From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

2019, *equal contribution (conference) Submitted

Abstract

Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

arXiv [BibTex]

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B. From Variational to Deterministic Autoencoders 2019, *equal contribution (conference) Submitted

arXiv [BibTex]

Fisher Efficient Inference of Intractable Models

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y.

Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

arXiv [BibTex]

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y. Fisher Efficient Inference of Intractable Models Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

arXiv [BibTex]

2016

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]

2016

Simon-Gabriel*, C. J., Ścibior*, A., Tolstikhin, I., Schölkopf, B. Consistent Kernel Mean Estimation for Functions of Random Variables 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]

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]

Bauer, M., van der Wilk, M., Rasmussen, C. E. Understanding Probabilistic Sparse Gaussian Process Approximations 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]

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Tolstikhin, I., Sriperumbudur, B. K., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1930-1938, (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]

Tolstikhin, I., Sriperumbudur, B. K., Schölkopf, B. Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels Advances in Neural Information Processing Systems 29, pages: 1930-1938, (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]

Local-utopia Policy Selection for Multi-objective Reinforcement Learning

Parisi, S., Blank, A., Viernickel, T., Peters, J.

In IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pages: 1-7, IEEE, December 2016 (inproceedings)

DOI [BibTex]

Parisi, S., Blank, A., Viernickel, T., Peters, J. Local-utopia Policy Selection for Multi-objective Reinforcement Learning In IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pages: 1-7, IEEE, December 2016 (inproceedings)

DOI [BibTex]

Lifelong Learning with Weighted Majority Votes

Pentina, A., Urner, R.

Advances in Neural Information Processing Systems 29, pages: 3612-3620, (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]

Pentina, A., Urner, R. Lifelong Learning with Weighted Majority Votes Advances in Neural Information Processing Systems 29, pages: 3612-3620, (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]

Active Nearest-Neighbor Learning in Metric Spaces

Kontorovich, A., Sabato, S., Urner, R.

Advances in Neural Information Processing Systems 29, pages: 856-864, (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]

Kontorovich, A., Sabato, S., Urner, R. Active Nearest-Neighbor Learning in Metric Spaces Advances in Neural Information Processing Systems 29, pages: 856-864, (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]

Catching heuristics are optimal control policies

Belousov, B., Neumann, G., Rothkopf, C., Peters, J.

Advances in Neural Information Processing Systems 29, pages: 1426-1434, (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]

Belousov, B., Neumann, G., Rothkopf, C., Peters, J. Catching heuristics are optimal control policies Advances in Neural Information Processing Systems 29, pages: 1426-1434, (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]

Incremental Imitation Learning of Context-Dependent Motor Skills

Ewerton, M., Maeda, G., Kollegger, G., Wiemeyer, J., Peters, J.

IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 351-358, IEEE, November 2016 (conference)

DOI Project Page [BibTex]

Ewerton, M., Maeda, G., Kollegger, G., Wiemeyer, J., Peters, J. Incremental Imitation Learning of Context-Dependent Motor Skills IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 351-358, IEEE, November 2016 (conference)

DOI Project Page [BibTex]