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2019


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DeepOBS: A Deep Learning Optimizer Benchmark Suite

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

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

link (url) [BibTex]

2019

link (url) [BibTex]


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

link (url) [BibTex]

link (url) [BibTex]


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Riemannian Adaptive Optimization Methods

Becigneul, G., Ganea, O.

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

link (url) [BibTex]

link (url) [BibTex]


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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]

link (url) [BibTex]


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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), pages: 1-6, Association for Computing Machinery, May 2019 (conference)

DOI [BibTex]

DOI [BibTex]


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Poincaré GloVe: Hyperbolic Word Embeddings

Tifrea*, A., Becigneul*, G., Ganea*, O.

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

link (url) [BibTex]

link (url) [BibTex]


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Resampled Priors for Variational Autoencoders

Bauer, M., Mnih, A.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 66-75, Proceedings of Machine Learning Research, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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

von Kügelgen, J., Mey, A., Loog, M.

In 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 (inproceedings)

PDF Poster link (url) [BibTex]

PDF Poster link (url) [BibTex]


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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]

PDF link (url) [BibTex]


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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]

PDF link (url) [BibTex]


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

PDF link (url) [BibTex]


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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]

PDF PDF link (url) [BibTex]


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Automatic Bayesian Density Analysis

Vergari, A., Molina, A., Peharz, R., Ghahramani, Z., Kersting, K., Valera, I.

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-19), 33:01, pages: 5207-5215, AAAI.org, AAAI-19, January 2019 (conference)

arXiv DOI [BibTex]

arXiv DOI [BibTex]


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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]

PDF link (url) [BibTex]


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A Kernel Stein Test for Comparing Latent Variable Models

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

2019 (conference) Submitted

arXiv [BibTex]

arXiv [BibTex]


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Feature extraction from the Hermitian manifold for Brain-Computer Interfaces

Xu, J., Jayaram, V., Schölkopf, B., Grosse-Wentrup, M.

9th International IEEE/EMBS Conference on Neural Engineering (NER), pages: 965-968, IEEE, 2019 (conference) In press

DOI [BibTex]

DOI [BibTex]

2018


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Enhancing the Accuracy and Fairness of Human Decision Making

Valera, I., Singla, A., Gomez Rodriguez, M.

Advances in Neural Information Processing Systems 31, pages: 1774-1783, (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)

arXiv link (url) Project Page [BibTex]

2018

arXiv link (url) Project Page [BibTex]


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Non-factorised Variational Inference in Dynamical Systems

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

1st Symposion on Advances in Approximate Bayesian Inference, December 2018 (conference)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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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]


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Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference

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

Workshop on Meta-Learning (MetaLearn 2018) at the 32nd Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

link (url) [BibTex]

link (url) [BibTex]


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Versa: Versatile and Efficient Few-shot Learning

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

Third Workshop on Bayesian Deep Learning at the 32nd Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)

link (url) [BibTex]

link (url) [BibTex]


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DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning

Harder, F., Köhler, J., Welling, M., Park, M.

Workshop on Privacy Preserving Machine Learning at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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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]


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Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems

Mehrjou, A., Schölkopf, B.

Workshop: Infer to Control: Probabilistic Reinforcement Learning and Structured Control at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Data-Efficient Hierarchical Reinforcement Learning

Nachum, O., Gu, S., Lee, H., Levine, S.

Advances in Neural Information Processing Systems 31, pages: 3307-3317, (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]


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Resampled Priors for Variational Autoencoders

Bauer, M., Mnih, A.

Third Workshop on Bayesian Deep Learning at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Generalisation in humans and deep neural networks

Geirhos, R., Temme, C. R. M., Rauber, J., Schütt, H., Bethge, M., Wichmann, F. A.

Advances in Neural Information Processing Systems 31, pages: 7549-7561, (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) [BibTex]

link (url) [BibTex]


Assessing Generative Models via Precision and Recall
Assessing Generative Models via Precision and Recall

Sajjadi, M. S. M., Bachem, O., Lucic, M., Bousquet, O., Gelly, S.

Advances in Neural Information Processing Systems 31, pages: 5234-5243, (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)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B.

Advances in Neural Information Processing Systems 31, pages: 9838-9848, (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)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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A Computational Camera with Programmable Optics for Snapshot High Resolution Multispectral Imaging

Chen, J., Hirsch, M., Eberhardt, B., Lensch, H. P. A.

Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, December 2018 (conference) Accepted

[BibTex]

[BibTex]


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Generalization in anti-causal learning

Kilbertus*, N., Parascandolo*, G., Schölkopf*, B.

NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, December 2018, *authors are listed in alphabetical order (conference)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


Efficient Encoding of Dynamical Systems through Local Approximations
Efficient Encoding of Dynamical Systems through Local Approximations

Solowjow, F., Mehrjou, A., Schölkopf, B., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 6073 - 6079 , Miami, Fl, USA, December 2018 (inproceedings)

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


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Informative Features for Model Comparison

Jitkrittum, W., Kanagawa, H., Sangkloy, P., Hays, J., Schölkopf, B., Gretton, A.

Advances in Neural Information Processing Systems 31, pages: 816-827, (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]


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Bayesian Nonparametric Hawkes Processes

Kapoor, J., Vergari, A., Gomez Rodriguez, M., Valera, I.

Bayesian Nonparametrics workshop at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Hyperbolic Neural Networks

Ganea*, O., Becigneul*, G., Hofmann, T.

Advances in Neural Information Processing Systems 31, pages: 5350-5360, (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)

link (url) [BibTex]

link (url) [BibTex]


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Regularizing Reinforcement Learning with State Abstraction

Akrour, R., Veiga, F., Peters, J., Neuman, G.

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018 (conference) Accepted

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning to Categorize Bug Reports with LSTM Networks

Gondaliya, K., Peters, J., Rueckert, E.

Proceedings of the 10th International Conference on Advances in System Testing and Validation Lifecycle (VALID), pages: 7-12, October 2018 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment

Muratore, F., Treede, F., Gienger, M., Peters, J.

2nd Annual Conference on Robot Learning (CoRL), 87, pages: 700-713, Proceedings of Machine Learning Research, PMLR, October 2018 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Reinforcement Learning of Phase Oscillators for Fast Adaptation to Moving Targets

Maeda, G., Koc, O., Morimoto, J.

Proceedings of The 2nd Conference on Robot Learning (CoRL), 87, pages: 630-640, (Editors: Aude Billard, Anca Dragan, Jan Peters, Jun Morimoto ), PMLR, October 2018 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Constraint-Space Projection Direct Policy Search

Akrour, R., Peters, J., Neuman, G.

14th European Workshop on Reinforcement Learning (EWRL), October 2018 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Spatio-temporal Transformer Network for Video Restoration

Kim, T. H., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.

15th European Conference on Computer Vision (ECCV), Part III, 11207, pages: 111-127, Lecture Notes in Computer Science, (Editors: Vittorio Ferrari, Martial Hebert,Cristian Sminchisescu and Yair Weiss), Springer, September 2018 (conference)

DOI [BibTex]

DOI [BibTex]


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Separating Reflection and Transmission Images in the Wild

Wieschollek, P., Gallo, O., Gu, J., Kautz, J.

European Conference on Computer Vision (ECCV), September 2018 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Risk-Sensitivity in Simulation Based Online Planning

Schmid, K., Belzner, L., Kiermeier, M., Neitz, A., Phan, T., Gabor, T., Linnhoff, C.

KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, pages: 229-240, (Editors: F. Trollmann and A. Y. Turhan), Springer, Cham, September 2018 (conference)

DOI [BibTex]

DOI [BibTex]


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The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution

Gondal, M. W., Schölkopf, B., Hirsch, M.

Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), September 2018 (conference)

arXiv URL [BibTex]

arXiv URL [BibTex]


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From Deterministic ODEs to Dynamic Structural Causal Models

Rubenstein, P. K., Bongers, S., Schölkopf, B., Mooij, J. M.

Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), August 2018 (conference)

Arxiv link (url) [BibTex]

Arxiv link (url) [BibTex]


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Generalized Score Functions for Causal Discovery

Huang, B., Zhang, K., Lin, Y., Schölkopf, B., Glymour, C.

Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages: 1551-1560, (Editors: Yike Guo and Faisal Farooq), ACM, August 2018 (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Yurtsever, A., Fercoq, O., Locatello, F., Cevher, V.

Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 5713-5722, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]