Lioutikov, R., Maeda, G., Veiga, F., Kersting, K., Peters, J.
Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives
IEEE International Conference on Robotics and Automation, (ICRA), pages: 1-8, IEEE, May 2018 (conference)
Mroueh, Y., Li*, C., Sercu*, T., Raj*, A., Cheng, Y.
Sobolev GAN
6th International Conference on Learning Representations (ICLR), May 2018, *equal contribution (conference)
Pong*, V., Gu*, S., Dalal, M., Levine, S.
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
6th International Conference on Learning Representations (ICLR), May 2018, *equal contribution (conference)
Rubenstein, P. K., Schölkopf, B., Tolstikhin, I.
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders
Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Eysenbach, B., Gu, S., Ibarz, J., Levine, S.
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Sajjadi, M. S. M., Parascandolo, G., Mehrjou, A., Schölkopf, B.
Tempered Adversarial Networks
Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Koert, D., Maeda, G., Neumann, G., Peters, J.
Learning Coupled Forward-Inverse Models with Combined Prediction Errors
IEEE International Conference on Robotics and Automation, (ICRA), pages: 2433-2439, IEEE, May 2018 (conference)
Rubenstein, P. K., Schölkopf, B., Tolstikhin, I.
Learning Disentangled Representations with Wasserstein Auto-Encoders
Workshop at the 6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Bauer, M., Volchkov, V., Hirsch, M., Schölkopf, B.
Automatic Estimation of Modulation Transfer Functions
IEEE International Conference on Computational Photography (ICCP), May 2018 (conference)
Rojas-Carulla, M., Baroni, M., Lopez-Paz, D.
Causal Discovery Using Proxy Variables
Workshop at 6th International Conference on Learning Representations (ICLR), May 2018 (conference)
Pinsler, R., Akrour, R., Osa, T., Peters, J., Neumann, G.
Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences
IEEE International Conference on Robotics and Automation, (ICRA), pages: 596-601, IEEE, May 2018 (conference)
Besserve, M., Shajarisales, N., Schölkopf, B., Janzing, D.
Group invariance principles for causal generative models
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84, pages: 557-565, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)
Locatello, F., Khanna, R., Ghosh, J., Rätsch, G.
Boosting Variational Inference: an Optimization Perspective
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)
Blöbaum, P., Janzing, D., Washio, T., Shimizu, S., Schölkopf, B.
Cause-Effect Inference by Comparing Regression Errors
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) , 84, pages: 900-909, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)
Schwarz, K., Wieschollek, P., Lensch, H. P. A.
Will People Like Your Image? Learning the Aesthetic Space
2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pages: 2048-2057, March 2018 (conference)
Kim, J., Tabibian, B., Oh, A., Schölkopf, B., Gomez Rodriguez, M.
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), pages: 324-332, (Editors: Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek), ACM, Febuary 2018 (conference)
Ścibior, A., Kammar, O., Ghahramani, Z.
Functional Programming for Modular Bayesian Inference
Proceedings of the ACM on Functional Programming (ICFP), 2(Article No. 83):1-29, ACM, 2018 (conference)
Vergari, A., Molina, A., Peharz, R., Ghahramani, Z., Kersting, K., Valera, I.
Automatic Bayesian Density Analysis
2018 (conference) Submitted
Kilcher*, Y., Becigneul*, G., Hofmann, T.
Magic Tunnels
ICLR 2019, 2018, *equal contribution (conference) Submitted
Babbar, R., Schölkopf, B.
Adversarial Extreme Multi-label Classification
2018 (conference) Submitted
Raj, A., Stich, S.
k–SVRG: Variance Reduction for Large Scale Optimization
In 2018 (inproceedings) Submitted
Peharz, R., Vergari, A., Stelzner, K., Molina, A., Trapp, M., Kersting, K., Ghahramani, Z.
Probabilistic Deep Learning using Random Sum-Product Networks
2018 (conference) Submitted
Raj*, A., Law*, L., Sejdinovic*, D., Park, M.
A Differentially Private Kernel Two-Sample Test
2018, *equal contribution (conference) Submitted
Besserve, M., Sun, R., Schölkopf, B.
Counterfactuals uncover the modular structure of deep generative models
2018 (conference) Submitted
Schökopf, B.
Maschinelles Lernen: Entwicklung ohne Grenzen?
In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)
Becigneul, G., Ganea, O.
Riemannian Adaptive Optimization Methods
ICLR 2019, 2018 (conference) Submitted
Wichmann, F. A., Jäkel, F.
Methods in Psychophysics
In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)
Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., Ostermann, K., Moss, S. K., Heunen, C., Ghahramani, Z.
Denotational Validation of Higher-order Bayesian Inference
Proceedings of the ACM on Principles of Programming Languages (POPL), 2(Article No. 60):1-29, ACM, 2018 (conference)
Tifrea*, A., Becigneul*, G., Ganea*, O.
Poincaré GloVe: Hyperbolic Word Embeddings
ICLR 2019, 2018, *equal contribution (conference) Submitted
Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.
Transfer Learning for BCIs
In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)
Nakanishi, J., Mistry, M., Peters, J., Schaal, S.
Towards compliant humanoids: an experimental assessment of suitable task space position/orientation controllers
In IROS 2007, 2007, pages: 2520-2527, (Editors: Grant, E. , T. C. Henderson), IEEE Service Center, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2007 (inproceedings)
Arnoldi, E., Bruzzone, L., Carlin, L., Pedron, L., Persello, C.
Sistema avanzato per la classificazione delle aree agricole in immagini ad elevata risoluzione geometrica: applicazione al territorio del Trentino
In pages: 1-6, 11. Conferenza Nazionale ASITA, November 2007 (inproceedings)
Choi, I., Shin, H.
Performance Stabilization and Improvement in Graph-based Semi-supervised Learning with
Ensemble Method and Graph Sharpening
In Korean Data Mining Society Conference, pages: 257-262, Korean Data Mining Society, Seoul, Korea, Korean Data Mining Society Conference, November 2007 (inproceedings)
Nowozin, S., BakIr, G., Tsuda, K.
Discriminative Subsequence Mining for Action Classification
In ICCV 2007, pages: 1919-1923, IEEE Computer Society, Los Alamitos, CA, USA, 11th IEEE International Conference on Computer Vision, October 2007 (inproceedings)
Eren, S., Grosse-Wentrup, M., Buss, M.
Unsupervised Classification for non-invasive Brain-Computer-Interfaces
In Automed 2007, pages: 65-66, VDI Verlag, Düsseldorf, Germany, Automed Workshop, October 2007 (inproceedings)
Smola, A., Gretton, A., Song, L., Schölkopf, B.
A Hilbert Space Embedding for Distributions
In Algorithmic Learning Theory, Lecture Notes in Computer Science 4754 , pages: 13-31, (Editors: M Hutter and RA Servedio and E Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory (ALT), October 2007 (inproceedings)
Maier, M., Hein, M., von Luxburg, U.
Cluster Identification in Nearest-Neighbor Graphs
In ALT 2007, pages: 196-210, (Editors: Hutter, M. , R. A. Servedio, E. Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory, October 2007 (inproceedings)
Altun, Y., Hofmann, T., Tsochantaridis, I.
Support Vector Machine Learning for Interdependent and Structured Output Spaces
In Predicting Structured Data, pages: 85-104, Advances in neural information processing systems, (Editors: Bakir, G. H. , T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, S. V. N. Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)
Laub, J., Macke, J., Müller, K., Wichmann, F.
Inducing Metric Violations in Human Similarity Judgements
In Advances in Neural Information Processing Systems 19, pages: 777-784, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)
Seeger, M.
Cross-Validation Optimization for Large Scale Hierarchical
Classification Kernel Methods
In Advances in Neural Information Processing Systems 19, pages: 1233-1240, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)
Wu, M., Schölkopf, B.
A Local Learning Approach for Clustering
In Advances in Neural Information Processing Systems 19, pages: 1529-1536, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)
Jegelka, S., Gretton, A.
Brisk Kernel ICA
In Large Scale Kernel Machines, pages: 225-250, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)
Grosse-Wentrup, M., Gramann, K., Buss, M.
Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces
In Advances in Neural Information Processing Systems 19, pages: 537-544, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)
Chapelle, O., Sindhwani, V., Keerthi, S.
Branch and Bound for Semi-Supervised Support Vector Machines
In Advances in Neural Information Processing Systems 19, pages: 217-224, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.
A Kernel Method for the Two-Sample-Problem
In Advances in Neural Information Processing Systems 19, pages: 513-520, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)
Keerthi, S., Sindhwani, V., Chapelle, O.
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models
In Advances in Neural Information Processing Systems 19, pages: 673-680, (Editors: Schölkopf, B. , J. Platt, T. Hofmann), MIT Press, Cambridge, MA, USA, Twentieth Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)
Steinke, F., Schölkopf, B., Blanz, V.
Learning Dense 3D Correspondence
In Advances in Neural Information Processing Systems 19, pages: 1313-1320, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)
Ulges, A., Lampert, CH., Keysers, D., Breuel, TM.
Optimal Dominant Motion Estimation using Adaptive Search of Transformation Space
In DAGM 2007, pages: 204-215, (Editors: Hamprecht, F. A., C. Schnörr, B. Jähne), Springer, Berlin, Germany, 29th Annual Symposium of the German Association for Pattern Recognition, September 2007 (inproceedings)
Chapelle, O.
Training a Support Vector Machine in the Primal
In Large Scale Kernel Machines, pages: 29-50, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007, This is a slightly updated version of the Neural Computation paper (inbook)
Quiñonero-Candela, J., Rasmussen, CE., Williams, CKI.
Approximation Methods for Gaussian Process Regression
In Large-Scale Kernel Machines, pages: 203-223, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)