Empirical Inference


2024


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Demonstration: Minsight - A Soft Vision-Based Tactile Sensor for Robotic Fingertips

Andrussow, I., Sun, H., Martius, G., Kuchenbecker, K. J.

Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (misc) Accepted

Abstract
Beyond vision and hearing, tactile sensing enhances a robot's ability to dexterously manipulate unfamiliar objects and safely interact with humans. Giving touch sensitivity to robots requires compact, robust, affordable, and efficient hardware designs, especially for high-resolution tactile sensing. We present a soft vision-based tactile sensor engineered to meet these requirements. Comparable in size to a human fingertip, Minsight uses machine learning to output high-resolution directional contact force distributions at 60 Hz. Minsight's tactile force maps enable precise sensing of fingertip contacts, which we use in this hands-on demonstration to allow a 3-DoF robot arm to physically track contact with a user's finger. While observing the colorful image captured by Minsight's internal camera, attendees can experience how its ability to detect delicate touches in all directions facilitates real-time robot interaction.

Project Page [BibTex]

2024

Project Page [BibTex]


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Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011

Bonse, M. J., Gebhard, T. D., Dannert, F. A., Absil, O., Cantalloube, F., Christiaens, V., Cugno, G., Garvin, E. O., Hayoz, J., Kasper, M., Matthews, E., Schölkopf, B., Quanz, S. P.

2024 (misc) Submitted

arXiv [BibTex]

arXiv [BibTex]

2023


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Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures

Jenny, D.

ETH Zurich, Switzerland, November 2023, external supervision (thesis)

[BibTex]

2023

[BibTex]


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Borges und die Künstliche Intelligenz

Bottou, L., Schölkopf, B.

2023, published in Frankfurter Allgemeine Zeitung, 18 December 2023, Nr. 294 (misc)

PDF [BibTex]

PDF [BibTex]


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Pulling back information geometry

Arvanitidis, G., González Duque, M., Pouplin, A., Kalatzis, D., Hauberg, S.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


A Robot Cluster for Reproducible Research in Dexterous Manipulation
A Robot Cluster for Reproducible Research in Dexterous Manipulation

Wüthrich*, M., Widmaier*, F., Bauer*, S., Funk, N., Urain, J., Peters, J., Watson, J., Chen, C., Srinivasan, K., Zhang, J., Zhang, J., Walter, M. R., Madan, R., Schaff, C., Maeda, T., Yoneda, T., Yarats, D., Allshire, A., Gordon, E. K., Bhattacharjee, T., Srinivasa, S. S., Garg, A., Buchholz, A., Stark, S., Steinbrenner, T., Akpo, J., Joshi, S., Agrawal, V., Schölkopf, B.

2021, *equal contribution (misc)

arXiv [BibTex]

arXiv [BibTex]


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Reinforcement Learning Algorithms: Analysis and Applications

Belousov, B., H., A., Klink, P., Parisi, S., Peters, J.

883, Studies in Computational Intelligence, Springer International Publishing, 2021 (book)

DOI [BibTex]

DOI [BibTex]


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On the Impact of Stable Ranks in Deep Nets

Georgiev, B., Franken, L., Mukherjee, M., Arvanitidis, G.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


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Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger

Allshire, A., Mittal, M., Lodaya, V., Makoviychuk, V., Makoviichuk, D., Widmaier, F., Wüthrich, M., Bauer, S., Handa, A., Garg, A.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


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Learning Neural Causal Models from Unknown Interventions

Ke, R., Bilaniuk, O., Goyal, A., Bauer, S., Larochelle, H., Schölkopf, B., Mozer, M. C., Pal, C., Bengio, Y.

2020 (misc)

arXiv Project Page [BibTex]

arXiv Project Page [BibTex]

2019


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Automatic Segmentation and Labelling for Robot Table Tennis Time Series

Lutz, P.

Technical University Darmstadt, Germany, August 2019 (thesis)

[BibTex]

2019

[BibTex]

2018


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Die kybernetische Revolution

Schölkopf, B.

S{\"u}ddeutsche Zeitung, 2018, (15-Mar-2018) (misc)

link (url) [BibTex]

2018

link (url) [BibTex]


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Large sample analysis of the median heuristic

Garreau, D., Jitkrittum, W., Kanagawa, M.

2018 (misc) In preparation

arXiv [BibTex]


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Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

PDF link (url) [BibTex]

PDF link (url) [BibTex]

2016


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Empirical Inference (2010-2015)
Scientific Advisory Board Report, 2016 (misc)

pdf [BibTex]

2016

pdf [BibTex]


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Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set

Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T.

2016 (misc)

Arxiv [BibTex]

2014


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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J., Peters, J.

97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

DOI [BibTex]

2014

DOI [BibTex]


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Development of advanced methods for improving astronomical images

Schmeißer, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)

[BibTex]

[BibTex]

2013


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Camera-specific Image Denoising

Schober, M.

Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

PDF [BibTex]

2013

PDF [BibTex]

2011


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Optimization for Machine Learning

Sra, S., Nowozin, S., Wright, S.

pages: 494, Neural information processing series, MIT Press, Cambridge, MA, USA, December 2011 (book)

Abstract
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Web [BibTex]

2011

Web [BibTex]


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Bayesian Time Series Models

Barber, D., Cemgil, A., Chiappa, S.

pages: 432, Cambridge University Press, Cambridge, UK, August 2011 (book)

[BibTex]

[BibTex]


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Handbook of Statistical Bioinformatics

Lu, H., Schölkopf, B., Zhao, H.

pages: 627, Springer Handbooks of Computational Statistics, Springer, Berlin, Germany, 2011 (book)

Web DOI [BibTex]

Web DOI [BibTex]

2010


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Inferring High-Dimensional Causal Relations using Free Probability Theory

Zscheischler, J.

Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

PDF [BibTex]

2010

PDF [BibTex]


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Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Shelton, J.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, Germany, July 2010 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Quantitative Evaluation of MR-based Attenuation Correction for Positron Emission Tomography (PET)

Mantlik, F.

Biologische Kybernetik, Universität Mannheim, Germany, March 2010 (diplomathesis)

[BibTex]

[BibTex]


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From Motor Learning to Interaction Learning in Robots

Sigaud, O., Peters, J.

pages: 538, Studies in Computational Intelligence ; 264, (Editors: O Sigaud, J Peters), Springer, Berlin, Germany, January 2010 (book)

Abstract
From an engineering standpoint, the increasing complexity of robotic systems and the increasing demand for more autonomously learning robots, has become essential. This book is largely based on the successful workshop "From motor to interaction learning in robots" held at the IEEE/RSJ International Conference on Intelligent Robot Systems. The major aim of the book is to give students interested the topics described above a chance to get started faster and researchers a helpful compandium.

Web DOI [BibTex]

Web DOI [BibTex]


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Finding Gene-Gene Interactions using Support Vector Machines

Rakitsch, B.

Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

[BibTex]

[BibTex]