Publications | Max Planck Institute for Intelligent Systems

ei Sliwa, J. Independent Mechanism Analysis for High Dimensions University of Tübingen, Germany, September 2022, (Graduate Training Centre of Neuroscience) (mastersthesis)

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On the Adversarial Robustness of Causal Algorithmic Recourse

Dominguez-Olmedo, R.

University of Tübingen, Germany, August 2022 (mastersthesis)

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ei Dominguez-Olmedo, R. On the Adversarial Robustness of Causal Algorithmic Recourse University of Tübingen, Germany, August 2022 (mastersthesis)

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Independent Mechanism Analysis in High-Dimensional Observation Spaces

Ghosh, S.

ETH Zurich, Switzerland, June 2022 (mastersthesis)

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ei Ghosh, S. Independent Mechanism Analysis in High-Dimensional Observation Spaces ETH Zurich, Switzerland, June 2022 (mastersthesis)

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Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR 2022)

Schölkopf, B., Uhler, C., Zhang, K.

177, Proceedings of Machine Learning Research, PMLR, April 2022 (proceedings)

link (url) [BibTex]

ei Schölkopf, B., Uhler, C., Zhang, K. Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR 2022) 177, Proceedings of Machine Learning Research, PMLR, April 2022 (proceedings)

link (url) [BibTex]

Voltage dependent investigations on the spin polarization of layered heterostructues

Miller, M.

Universität Stuttgart, Stuttgart, 2022 (mastersthesis)

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mms Miller, M. Voltage dependent investigations on the spin polarization of layered heterostructues Universität Stuttgart, Stuttgart, 2022 (mastersthesis)

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2021

Learning Neural Causal Models with Active Interventions

Scherrer, N.

ETH Zurich, Switzerland, November 2021 (mastersthesis)

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2021

ei Scherrer, N. Learning Neural Causal Models with Active Interventions ETH Zurich, Switzerland, November 2021 (mastersthesis)

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HealthGen: Conditional Generation of Realistic Medical Time Series with Informative Missingness

Bing, S.

ETH Zurich, Switzerland, October 2021 (mastersthesis)

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ei Bing, S. HealthGen: Conditional Generation of Realistic Medical Time Series with Informative Missingness ETH Zurich, Switzerland, October 2021 (mastersthesis)

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Study of the Interventional Consistency of Autoencoders

Lanzillotta, G.

ETH Zurich, Switzerland, October 2021 (mastersthesis)

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ei Lanzillotta, G. Study of the Interventional Consistency of Autoencoders ETH Zurich, Switzerland, October 2021 (mastersthesis)

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Training with Few to Manipulate Many. On OOD generalization in relational reinforcement learning

Mambelli, D.

ETH Zurich, Switzerland, October 2021 (mastersthesis)

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ei Mambelli, D. Training with Few to Manipulate Many. On OOD generalization in relational reinforcement learning ETH Zurich, Switzerland, October 2021 (mastersthesis)

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Proceedings of the 1st Workshop on NLP for Positive Impact

Field, A., Prabhumoye, S., Sap, M., Jin, Z., Zhao, J., Brockett, C.

Association for Computational Linguistics, August 2021 (proceedings)

link (url) [BibTex]

ei Field, A., Prabhumoye, S., Sap, M., Jin, Z., Zhao, J., Brockett, C. Proceedings of the 1st Workshop on NLP for Positive Impact Association for Computational Linguistics, August 2021 (proceedings)

link (url) [BibTex]

Robotic Surgery Training in AR: Multimodal Record and Replay

Krauthausen, F.

pages: 1-147, University of Stuttgart, Stuttgart, May 2021, Study Program in Software Engineering (mastersthesis)

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hi Krauthausen, F. Robotic Surgery Training in AR: Multimodal Record and Replay pages: 1-147, University of Stuttgart, Stuttgart, May 2021, Study Program in Software Engineering (mastersthesis)

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Direct detection of spin Hall effect induced torques in platinum/ferromagnetic bilayer systems

Alten, F.

Universität Stuttgart, Stuttgart, January 2021 (mastersthesis)

mms

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mms Alten, F. Direct detection of spin Hall effect induced torques in platinum/ferromagnetic bilayer systems Universität Stuttgart, Stuttgart, January 2021 (mastersthesis)

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Method of making one or more fibrils, computer implemented method of simulating an adhesive force of one or more fibrils and fibril

Sitti, M., Liimatainen, V.

2021, EP Prio. Patent App. 21 162 253.5 (mpi_year_book)

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pi Sitti, M., Liimatainen, V. Method of making one or more fibrils, computer implemented method of simulating an adhesive force of one or more fibrils and fibril 2021, EP Prio. Patent App. 21 162 253.5 (mpi_year_book)

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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]

ei Belousov, B., H., A., Klink, P., Parisi, S., Peters, J. Reinforcement Learning Algorithms: Analysis and Applications 883, Studies in Computational Intelligence, Springer International Publishing, 2021 (book)

DOI [BibTex]

Community detection in heterogeneously attributed networks

Zottino, N.

Politecnico di Torino, 2021 (mastersthesis)

pio

link (url) [BibTex]

pio Zottino, N. Community detection in heterogeneously attributed networks Politecnico di Torino, 2021 (mastersthesis)

link (url) [BibTex]

2020

Hydromagnonics: Manipulation of magnonic systems with hydrogen

Sauter, R.

Universität Stuttgart, Stuttgart, December 2020 (mastersthesis)

mms

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2020

mms Sauter, R. Hydromagnonics: Manipulation of magnonic systems with hydrogen Universität Stuttgart, Stuttgart, December 2020 (mastersthesis)

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A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Ahmed, O.

ETH Zurich, Switzerland, October 2020 (mastersthesis)

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ei Ahmed, O. A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning ETH Zurich, Switzerland, October 2020 (mastersthesis)

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A Comprehensive Benchmark Evaluation of Synthetic Data Generation for Biomedical Imaging

DuMont Schütte, A.

ETH Zurich, Switzerland, October 2020 (mastersthesis)

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ei DuMont Schütte, A. A Comprehensive Benchmark Evaluation of Synthetic Data Generation for Biomedical Imaging ETH Zurich, Switzerland, October 2020 (mastersthesis)

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Deep learning for the parameter estimation of tight-binding Hamiltonians

Cacioppo, A.

University of Roma, La Sapienza, Italy, May 2020 (mastersthesis)

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ei Cacioppo, A. Deep learning for the parameter estimation of tight-binding Hamiltonians University of Roma, La Sapienza, Italy, May 2020 (mastersthesis)

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Learning Algorithms, Invariances, and the Real World

Zecevic, M.

Technical University of Darmstadt, Germany, April 2020 (mastersthesis)

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ei Zecevic, M. Learning Algorithms, Invariances, and the Real World Technical University of Darmstadt, Germany, April 2020 (mastersthesis)

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Interaction of hydrogen isotopes with flexible metal-organic frameworks

Bondorf, L.

Universität Stuttgart, Stuttgart, February 2020 (mastersthesis)

mms

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mms Bondorf, L. Interaction of hydrogen isotopes with flexible metal-organic frameworks Universität Stuttgart, Stuttgart, February 2020 (mastersthesis)

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Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

Marco, A., Rohr, A. V., Baumann, D., Hernández-Lobato, J. M., Trimpe, S.

2020 (proceedings) In revision

Abstract

When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.

am ics

arXiv code (python) PDF [BibTex]

am ics Marco, A., Rohr, A. V., Baumann, D., Hernández-Lobato, J. M., Trimpe, S. Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures 2020 (proceedings) In revision

arXiv code (python) PDF [BibTex]

Developing new methods for routing and optimal transport on networks

Lonardi, A.

Università degli studi di Padova, 2020 (mastersthesis)

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pdf [BibTex]

pio Lonardi, A. Developing new methods for routing and optimal transport on networks Università degli studi di Padova, 2020 (mastersthesis)

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Edge-Disjoint Path Problem on Stochastic Block Models through Message Passing

Lorenzo Ferretti

Sapienza Università di Roma, 2020 (mastersthesis)

pio

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pio Lorenzo Ferretti Edge-Disjoint Path Problem on Stochastic Block Models through Message Passing Sapienza Università di Roma, 2020 (mastersthesis)

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Colloidal particles supporting urase activity

Baldauf, A.

Univ. of Stuttgart, 2020 (mastersthesis)

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pf Baldauf, A. Colloidal particles supporting urase activity Univ. of Stuttgart, 2020 (mastersthesis)

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Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Janai, J., Güney, F., Behl, A., Geiger, A.

Arxiv, Foundations and Trends in Computer Graphics and Vision, 2020 (book)

Abstract

Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This monograph attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.

avg

pdf Project Page link Project Page [BibTex]

avg Janai, J., Güney, F., Behl, A., Geiger, A. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art Arxiv, Foundations and Trends in Computer Graphics and Vision, 2020 (book)

pdf Project Page link Project Page [BibTex]

Diffusion studies on biomolecules by NMR

Bochert, I.

Univ. of Stuttgart, 2020 (mastersthesis)

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pf Bochert, I. Diffusion studies on biomolecules by NMR Univ. of Stuttgart, 2020 (mastersthesis)

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2019

Analysis and modelling of information ecosystems

Emanuele Pigani

Università degli studi di Padova, October 2019 (mastersthesis)

pio

link (url) [BibTex]

2019

pio Emanuele Pigani Analysis and modelling of information ecosystems Università degli studi di Padova, October 2019 (mastersthesis)

link (url) [BibTex]

Inferring the Band Structure from Band Mapping Data through Machine Learning

Stimper, V.

Technical University of Munich, September 2019 (mastersthesis)

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ei Stimper, V. Inferring the Band Structure from Band Mapping Data through Machine Learning Technical University of Munich, September 2019 (mastersthesis)

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A new approach for community detection in multilayer networks

Contisciani, M.

Università degli studi di Padova, September 2019 (mastersthesis)

pio

link (url) [BibTex]

pio Contisciani, M. A new approach for community detection in multilayer networks Università degli studi di Padova, September 2019 (mastersthesis)

link (url) [BibTex]

Learning to Diagnose Diabetes from Magnetic Resonance Tomography

Dietz, B.

ETH Zurich, Switzerland, August 2019 (mastersthesis)

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ei Dietz, B. Learning to Diagnose Diabetes from Magnetic Resonance Tomography ETH Zurich, Switzerland, August 2019 (mastersthesis)

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Reinforcement Learning for a Two-Robot Table Tennis Simulation

Li, G.

RWTH Aachen University, Germany, July 2019 (mastersthesis)

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ei Li, G. Reinforcement Learning for a Two-Robot Table Tennis Simulation RWTH Aachen University, Germany, July 2019 (mastersthesis)

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Characteristics of longitudinal physiological measurements of late-stage ALS patients

Konieczny, L.

Ludwig-Maximilians-Universität München, Germany, May 2019 (mastersthesis)

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ei Konieczny, L. Characteristics of longitudinal physiological measurements of late-stage ALS patients Ludwig-Maximilians-Universität München, Germany, May 2019 (mastersthesis)

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X-ray microscopic characterization of high-Tc-supercoductors using image processing

Bihler, M.

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

mms

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mms Bihler, M. X-ray microscopic characterization of high-Tc-supercoductors using image processing Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

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Active matter and self propelled microparticles

Kottapalli, S. N. M.

Univ. of Stuttgart, 2019 (mastersthesis)

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pf Kottapalli, S. N. M. Active matter and self propelled microparticles Univ. of Stuttgart, 2019 (mastersthesis)

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Dynamic hierarchical ranking in directed networks: from static SpringRank to dynamic and bayesian models using variational techniques

Vecchia, A. D.

Università degli studi di Padova, 2019 (mastersthesis)

pio

pdf [BibTex]

pio Vecchia, A. D. Dynamic hierarchical ranking in directed networks: from static SpringRank to dynamic and bayesian models using variational techniques Università degli studi di Padova, 2019 (mastersthesis)

pdf [BibTex]

Scientific Report 2016 - 2018

2019 (mpi_year_book)

Abstract

This report presents research done at the Max Planck Institute for Intelligent Systems from January 2016 to December 2018. It is our third report since the founding of the institute in 2011. This status report is organized as follows: we begin with an overview of the institute, including its organizational structure (Chapter 1). The central part of the scientific report consists of chapters on the research conducted by the institute’s departments (Chapters 2 to 5) and its independent research groups (Chapters 6 to 18), as well as the work of the institute’s central scientific facilities (Chapter 19). For entities founded after January 2016, the respective report sections cover work done from the date of the establishment of the department, group, or facility.

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Scientific Report 2016 - 2018 [BibTex]