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2018


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Group invariance principles for causal generative models

Besserve, M., Shajarisales, N., Schölkopf, B., Janzing, D.

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)

ei

link (url) [BibTex]

2018


link (url) [BibTex]


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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)

ei

link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]


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Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations

Manschitz, S., Gienger, M., Kober, J., Peters, J.

IEEE Robotics and Automation Letters, 3(2):926-933, April 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Probabilistic movement primitives under unknown system dynamics

Paraschos, A., Rueckert, E., Peters, J., Neumann, G.

Advanced Robotics, 32(6):297-310, April 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Cause-Effect Inference by Comparing Regression Errors

Blöbaum, P., Janzing, D., Washio, T., Shimizu, S., Schölkopf, B.

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)

ei

link (url) [BibTex]

link (url) [BibTex]


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Will People Like Your Image? Learning the Aesthetic Space

Schwarz, K., Wieschollek, P., Lensch, H. P. A.

2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pages: 2048-2057, March 2018 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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An Algorithmic Perspective on Imitation Learning

Osa, T., Pajarinen, J., Neumann, G., Bagnell, J., Abbeel, P., Peters, J.

Foundations and Trends in Robotics, 7(1-2):1-179, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Using Probabilistic Movement Primitives in Robotics

Paraschos, A., Daniel, C., Peters, J., Neumann, G.

Autonomous Robots, 42(3):529-551, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Representation of sensory uncertainty in macaque visual cortex

Goris, R., Henaff, O., Meding, K.

Computational and Systems Neuroscience (COSYNE) 2018, March 2018 (poster)

ei

[BibTex]

[BibTex]


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A kernel-based approach to learning contact distributions for robot manipulation tasks

Kroemer, O., Leischnig, S., Luettgen, S., Peters, J.

Autonomous Robots, 42(3):581-600, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Thumb xl singh et al 2018 advanced functional materials
Photogravitactic Microswimmers

Singh, D. P., Uspal, W. E., Popescu, M. N., Wilson, L. G., Fischer, P.

Adv. Func. Mat., 28, pages: 1706660, Febuary 2018 (article)

Abstract
Abstract Phototactic microorganisms are commonly observed to respond to natural sunlight by swimming upward against gravity. This study demonstrates that synthetic photochemically active microswimmers can also swim against gravity. The particles initially sediment and, when illuminated at low light intensities exhibit wall‐bound states of motion near the bottom surface. Upon increasing the intensity of light, the artificial swimmers lift off from the wall and swim against gravity and away from the light source. This motion in the bulk has been further confirmed using holographic microscopy. A theoretical model is presented within the framework of self‐diffusiophoresis, which allows to unequivocally identify the photochemical activity and the phototactic response as key mechanisms in the observed phenomenology. Since the lift‐off threshold intensity depends on the particle size, it can be exploited to selectively address particles with the same density from a polydisperse mixture of active particles and move them in or out of the boundary region. This study provides a simple design strategy to fabricate artificial microswimmers whose two‐ or three‐dimensional swimming behavior can be controlled with light.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl matuschek et al 2018 small
Chiral Plasmonic Hydrogen Sensors

Matuschek, M., Singh, D. P., Hyeon-Ho, J., Nesterov, M., Weiss, T., Fischer, P., Neubrech, F., Na Liu, L.

Small, 14(7):1702990, Febuary 2018 (article)

Abstract
In this article, a chiral plasmonic hydrogen‐sensing platform using palladium‐based nanohelices is demonstrated. Such 3D chiral nanostructures fabricated by nanoglancing angle deposition exhibit strong circular dichroism both experimentally and theoretically. The chiroptical properties of the palladium nanohelices are altered upon hydrogen uptake and sensitively depend on the hydrogen concentration. Such properties are well suited for remote and spark‐free hydrogen sensing in the flammable range. Hysteresis is reduced, when an increasing amount of gold is utilized in the palladium‐gold hybrid helices. As a result, the linearity of the circular dichroism in response to hydrogen is significantly improved. The chiral plasmonic sensor scheme is of potential interest for hydrogen‐sensing applications, where good linearity and high sensitivity are required.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Kim, J., Tabibian, B., Oh, A., Schölkopf, B., Gomez Rodriguez, M.

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)

ei

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


Thumb xl fig1b
Acoustic Fabrication via the Assembly and Fusion of Particles

Melde, K., Choi, E., Wu, Z., Palagi, S., Qiu, T., Fischer, P.

Advanced Materials, 30(3):1704507, January 2018 (article)

Abstract
Acoustic assembly promises a route toward rapid parallel fabrication of whole objects directly from solution. This study reports the contact-free and maskless assembly, and fixing of silicone particles into arbitrary 2D shapes using ultrasound fields. Ultrasound passes through an acoustic hologram to form a target image. The particles assemble from a suspension along lines of high pressure in the image due to acoustic radiation forces and are then fixed (crosslinked) in a UV-triggered reaction. For this, the particles are loaded with a photoinitiator by solvent-induced swelling. This localizes the reaction and allows the bulk suspension to be reused. The final fabricated parts are mechanically stable and self-supporting.

pf

link (url) DOI Project Page [BibTex]


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Approximate Value Iteration Based on Numerical Quadrature

Vinogradska, J., Bischoff, B., Peters, J.

IEEE Robotics and Automation Letters, 3(2):1330-1337, January 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review

Yi, Z., Zhang, Y., Peters, J.

Sensors and Actuators A: Physical, 269, pages: 41-52, January 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Die kybernetische Revolution

Schölkopf, B.

15-Mar-2018, Süddeutsche Zeitung, 2018 (misc)

ei

link (url) [BibTex]

link (url) [BibTex]


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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)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Design and Analysis of the NIPS 2016 Review Process

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.

Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (article)

ei slt

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

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


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A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K.

Journal of Machine Learning, 2018 (article) Accepted

ei

Project Page [BibTex]

Project Page [BibTex]


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Automatic Bayesian Density Analysis

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

2018 (conference) Submitted

ei

arXiv [BibTex]

arXiv [BibTex]


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A virtual reality environment for experiments in assistive robotics and neural interfaces

Bustamante, S.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Does universal controllability of physical systems prohibit thermodynamic cycles?

Janzing, D., Wocjan, P.

Open Systems and Information Dynamics, 25(3):1850016, 2018 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Optimal Trajectory Generation and Learning Control for Robot Table Tennis

Koc, O.

Technical University Darmstadt, Germany, 2018 (phdthesis)

ei

[BibTex]

[BibTex]


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Learning Causality and Causality-Related Learning: Some Recent Progress

Zhang, K., Schölkopf, B., Spirtes, P., Glymour, C.

National Science Review, 5(1):26-29, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Online optimal trajectory generation for robot table tennis

Koc, O., Maeda, G., Peters, J.

Robotics and Autonomous Systems, 105, pages: 121-137, 2018 (article)

ei

PDF link (url) DOI Project Page [BibTex]

PDF link (url) DOI Project Page [BibTex]


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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract
This paper introduces a novel Hilbert space representation of a counterfactual distribution---called counterfactual mean embedding (CME)---with applications in nonparametric causal inference. Counterfactual prediction has become an ubiquitous tool in machine learning applications, such as online advertisement, recommendation systems, and medical diagnosis, whose performance relies on certain interventions. To infer the outcomes of such interventions, we propose to embed the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel. Under appropriate assumptions, the CME allows us to perform causal inference over the entire landscape of the counterfactual distribution. The CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also derive a rate of convergence which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Our framework can deal with not only real-valued outcome, but potentially also more complex and structured outcomes such as images, sequences, and graphs. Lastly, our experimental results on off-policy evaluation tasks demonstrate the advantages of the proposed estimator.

ei pn

arXiv [BibTex]

arXiv [BibTex]


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L4: Practical loss-based stepsize adaptation for deep learning

Rolinek, M., Martius, G.

In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), pages: 6434-6444, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 2018 (inproceedings)

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Github link (url) Project Page [BibTex]

Github link (url) Project Page [BibTex]


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Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions

Osa, T., Peters, J., Neumann, G.

Advanced Robotics, 32(18):955-968, 2018 (article)

ei

DOI Project Page [BibTex]


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k–SVRG: Variance Reduction for Large Scale Optimization

Raj, A., Stich, S.

In 2018 (inproceedings) Submitted

ei

[BibTex]

[BibTex]


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Distribution-Dissimilarities in Machine Learning

Simon-Gabriel, C. J.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei

[BibTex]

[BibTex]


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Probabilistic Deep Learning using Random Sum-Product Networks

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

2018 (conference) Submitted

ei

arXiv [BibTex]

arXiv [BibTex]


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Autofocusing-based phase correction

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

Magnetic Resonance in Medicine, 80(3):958-968, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


Thumb xl publication alife 2018
Systematic self-exploration of behaviors for robots in a dynamical systems framework

Pinneri, C., Martius, G.

In Proc. Artificial Life XI, pages: 319-326, MIT Press, Cambridge, MA, 2018 (inproceedings)

Abstract
One of the challenges of this century is to understand the neural mechanisms behind cognitive control and learning. Recent investigations propose biologically plausible synaptic mechanisms for self-organizing controllers, in the spirit of Hebbian learning. In particular, differential extrinsic plasticity (DEP) [Der and Martius, PNAS 2015], has proven to enable embodied agents to self-organize their individual sensorimotor development, and generate highly coordinated behaviors during their interaction with the environment. These behaviors are attractors of a dynamical system. In this paper, we use the DEP rule to generate attractors and we combine it with a “repelling potential” which allows the system to actively explore all its attractor behaviors in a systematic way. With a view to a self-determined exploration of goal-free behaviors, our framework enables switching between different motion patterns in an autonomous and sequential fashion. Our algorithm is able to recover all the attractor behaviors in a toy system and it is also effective in two simulated environments. A spherical robot discovers all its major rolling modes and a hexapod robot learns to locomote in 50 different ways in 30min.

al

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Generalized phase locking analysis of electrophysiology data

Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N. K., Besserve, M.

7th AREADNE Conference on Research in Encoding and Decoding of Neural Ensembles, 2018 (poster)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Case series: Slowing alpha rhythm in late-stage ALS patients

Hohmann, M. R., Fomina, T., Jayaram, V., Emde, T., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

Clinical Neurophysiology, 129(2):406-408, 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

Šošić, A., Rueckert, E., Peters, J., Zoubir, A., Koeppl, H.

Journal of Machine Learning Research, 19(69):1-45, 2018 (article)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Grip Stabilization of Novel Objects using Slip Prediction

Veiga, F., Peters, J., Hermans, T.

IEEE Transactions on Haptics, 2018 (article) In press

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Domain Adaptation Under Causal Assumptions

Lechner, T.

Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

ei

[BibTex]

[BibTex]


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A Differentially Private Kernel Two-Sample Test

Raj*, A., Law*, L., Sejdinovic*, D., Park, M.

2018, *equal contribution (conference) Submitted

ei

[BibTex]

[BibTex]


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Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis—implications for brain–computer interfacing

Kellmeyer, P., Grosse-Wentrup, M., Schulze-Bonhage, A., Ziemann, U., Ball, T.

Journal of Neural Engineering, 15(4):041003, IOP Publishing, 2018 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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A Causal Perspective on Deep Representation Learning

Suter, R.

ETH Zurich, 2018 (mastersthesis)

ei

[BibTex]


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Quantum machine learning: a classical perspective

Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., Wossnig, L.

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474(2209):20170551, 2018 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Kernel-based tests for joint independence

Pfister, N., Bühlmann, P., Schölkopf, B., Peters, J.

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1):5-31, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]