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Institute Talks

Improving the Gaussian Mechanism for Differential Privacy

IS Colloquium
  • 27 June 2018 • 14:15 15:15
  • Borja de Balle Pigem
  • MPI IS lecture hall (N0.002)

The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms. In this talk I will revisit the classical analysis of the Gaussian mechanism and show it has several important limitations. For example, our analysis reveals that the variance formula for the original mechanism is far from tight in the high privacy regime and that it cannot be extended to the low privacy regime. We address these limitations by developing a new Gaussian mechanism whose variance is optimally calibrated by solving an equation involving the Gaussian cumulative density function. Our analysis side-steps the use of tail bounds approximations and relies on a novel characterisation of differential privacy that might be of independent interest. We numerically show that analytical calibration removes at least a third of the variance of the noise compared to the classical Gaussian mechanism. We also propose to equip the Gaussian mechanism with a post-processing step based on adaptive denoising estimators by leveraging that the variance of the perturbation is known. Experiments with synthetic and real data show that this denoising step yields dramatic accuracy improvements in the high-dimensional regime. Based on joint work with Y.-X. Wang to appear at ICML 2018. Pre-print: https://arxiv.org/abs/1805.06530

Organizers: Michel Besserve Isabel Valera

Learning Control for Intelligent Physical Systems

Talk
  • 13 July 2018 • 14:15 14:45
  • Dr. Sebastian Trimpe
  • MPI-IS, Stuttgart, Lecture Hall 2 D5

Modern technology allows us to collect, process, and share more data than ever before. This data revolution opens up new ways to design control and learning algorithms, which will form the algorithmic foundation for future intelligent systems that shall act autonomously in the physical world. Starting from a discussion of the special challenges when combining machine learning and control, I will present some of our recent research in this exciting area. Using the example of the Apollo robot learning to balance a stick in its hand, I will explain how intelligent agents can learn new behavior from just a few experimental trails. I will also discuss the need for theoretical guarantees in learning-based control, and how we can obtain them by combining learning and control theory.

Organizers: Katherine Kuchenbecker Ildikó Papp-Wiedmann Matthias Tröndle Claudia Daefler

Household Assistants: the Path from the Care-o-bot Vision to First Products

Talk
  • 13 July 2018 • 14:45 15:15
  • Dr. Martin Hägele
  • MPI-IS, Stuttgart, Lecture Hall 2 D5

In 1995 Fraunhofer IPA embarked on a mission towards designing a personal robot assistant for everyday tasks. In the following years Care-O-bot developed into a long-term experiment for exploring and demonstrating new robot technologies and future product visions. The recent fourth generation of the Care-O-bot, introduced in 2014 aimed at designing an integrated system which addressed a number of innovations such as modularity, “low-cost” by making use of new manufacturing processes, and advanced human-user interaction. Some 15 systems were built and the intellectual property (IP) generated by over 20 years of research was recently licensed to a start-up. The presentation will review the path from an experimental platform for building up expertise in various robotic disciplines to recent pilot applications based on the now commercial Care-O-bot hardware.

Organizers: Katherine Kuchenbecker Ildikó Papp-Wiedmann Matthias Tröndle Claudia Daefler

The Critical Role of Atoms at Surfaces and Interfaces: Do we really have control? Can we?

Talk
  • 13 July 2018 • 15:45 16:15
  • Prof. Dr. Dawn Bonnell
  • MPI-IS, Stuttgart, Lecture Hall 2 D5

With the ubiquity of catalyzed reactions in manufacturing, the emergence of the device laden internet of things, and global challenges with respect to water and energy, it has never been more important to understand atomic interactions in the functional materials that can provide solutions in these spaces.

Organizers: Katherine Kuchenbecker Ildikó Papp-Wiedmann Matthias Tröndle Claudia Daefler

Interactive Visualization – A Key Discipline for Big Data Analysis

Talk
  • 13 July 2018 • 15:00 15:30
  • Prof. Dr. Thomas Ertl
  • MPI-IS, Stuttgart, Lecture Hall 2 D5

Big Data has become the general term relating to the benefits and threats which result from the huge amount of data collected in all parts of society. While data acquisition, storage and access are relevant technical aspects, the analysis of the collected data turns out to be at the core of the Big Data challenge. Automatic data mining and information retrieval techniques have made much progress but many application scenarios remain in which the human in the loop plays an essential role. Consequently, interactive visualization techniques have become a key discipline of Big Data analysis and the field is reaching out to many new application domains. This talk will give examples from current visualization research projects at the University of Stuttgart demonstrating the thematic breadth of application scenarios and the technical depth of the employed methods. We will cover advances in scientific visualization of fields and particles, visual analytics of document collections and movement patterns as well as cognitive aspects.

Organizers: Katherine Kuchenbecker Ildikó Papp-Wiedmann Matthias Tröndle Claudia Daefler

Imitation of Human Motion Planning

Talk
  • 27 July 2018 • 12:00 12:45
  • Jim Mainprice
  • N3.022 (Aquarium)

Humans act upon their environment through motion, the ability to plan their movements is therefore an essential component of their autonomy. In recent decades, motion planning has been widely studied in robotics and computer graphics. Nevertheless robots still fail to achieve human reactivity and coordination. The need for more efficient motion planning algorithms has been present through out my own research on "human-aware" motion planning, which aims to take the surroundings humans explicitly into account. I believe imitation learning is the key to this particular problem as it allows to learn both, new motion skills and predictive models, two capabilities that are at the heart of "human-aware" robots while simultaneously holding the promise of faster and more reactive motion generation. In this talk I will present my work in this direction.

  • Dominik Bach

Under acute threat, biological agents need to choose adaptive actions to survive. In my talk, I will provide a decision-theoretic view on this problem and ask, what are potential computational algorithms for this choice, and how are they implemented in neural circuits. Rational design principles and non-human animal data tentatively suggest a specific architecture that heavily relies on tailored algorithms for specific threat scenarios. Virtual reality computer games provide an opportunity to translate non-human animal tasks to humans and investigate these algorithms across species. I will discuss the specific challenges for empirical inference on underlying neural circuits given such architecture.

Organizers: Michel Besserve


  • Anton Van Den Hengel
  • Aquarium

Visual Question Answering is one of the applications of Deep Learning that is pushing towards real Artificial Intelligence. It turns the typical deep learning process around by only defining the task to be carried out after the training has taken place, which changes the task fundamentally. We have developed a range of strategies for incorporating other information sources into deep learning-based methods, and the process taken a step towards developing algorithms which learn how to use other algorithms to solve a problem, rather than solving it directly. This talk thus covers some of the high-level questions about the types of challenges Deep Learning can be applied to, and how we might separate the things its good at from those that it’s not.

Organizers: Siyu Tang


The Gentle Robot

Talk
  • 27 September 2017 • 13:13 14:50
  • Prof. Sami Haddadin
  • Main Seminar Room (N0.002)

Enabling robots for interaction with humans and unknown environments has been one of the primary goals of robotics research over decades. I will outline how human-centered robot design, nonlinear soft-robotics control inspired by human neuromechanics and physics grounded learning algorithms will let robots become a commodity in our near-future society. In particular, compliant and energy-controlled ultra-lightweight systems capable of complex collision handling enable high-performance human assistance over a wide variety of application domains. Together with novel methods for dynamics and skill learning, flexible and easy-to-use robotic power tools and systems can be designed. Recently, our work has led to the first next generation robot Franka Emika that has recently become commercially available. The system is able to safely interact with humans, execute and even learn sensitive manipulation skills, is affordable and designed as a distributed interconnected system.

Organizers: Eva Laemmerhirt


Meta-learning statistics and augmentations for few shot learning

IS Colloquium
  • 25 September 2017 • 11:15 12:15
  • Amos Storkey
  • Tübingen, MPI_IS Lecture Hall (ground floor)

In this talk I introduce the neural statistician as an approach for meta learning. The neural statistician learns to appropriately summarise datasets through a learnt statistic vector. This can be used for few shot learning, by computing the statistic vectors for the presented data, and using these statistics as context variables for one-shot classification and generation. I will show how we can generalise the neural statistician to a context aware learner that learns to characterise and combine independently learnt contexts. I will also demonstrate an approach for meta-learning data augmentation strategies. Acknowledgments: This work is joint work with Harri Edwards, Antreas Antoniou, and Conor Durkan.

Organizers: Philipp Hennig


The Three Pillars of Fully Autonomous Driving

Talk
  • 18 September 2017 • 11:00 12:00
  • Prof. Amnon Shashua
  • MPI_IS Stuttgart, Lecture Room 2 D5

The field of transportation is undergoing a seismic change with the coming introduction of autonomous driving. The technologies required to enable computer driven cars involves the latest cutting edge artificial intelligence algorithms along three major thrusts: Sensing, Planning and Mapping. Prof. Amnon Shashua, Co-founder and Chairman of Mobileye, will describe the challenges and the kind of machine learning algorithms involved, but will do that through the perspective of Mobileye’s activity in this domain.


A locally Adaptive Normal Distribution

Talk
  • 05 September 2017 • 14:00 15:30
  • Georgios Arvanitidis
  • S2 Seminar Room

The fundamental building block in many learning models is the distance measure that is used. Usually, the linear distance is used for simplicity. Replacing this stiff distance measure with a flexible one could potentially give a better representation of the actual distance between two points. I will present how the normal distribution changes if the distance measure respects the underlying structure of the data. In particular, a Riemannian manifold will be learned based on observations. The geodesic curve can then be computed—a length-minimizing curve under the Riemannian measure. With this flexible distance measure we get a normal distribution that locally adapts to the data. A maximum likelihood estimation scheme is provided for inference of the parameters mean and covariance, and also, a systematic way to choose the parameter defining the Riemannian manifold. Results on synthetic and real world data demonstrate the efficiency of the proposed model to fit non-trivial probability distributions.

Organizers: Philipp Hennig


  • Prof. Dr. Hedvig Kjellström
  • N3.022 / Aquarium

In this talk I will first outline my different research projects. I will then focus on the EACare project, a quite newly started multi-disciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer's disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. I describe the scope and method of the project, and report on a first Wizard of Oz prototype.


  • Yeara Kozlov
  • Aquarium

Creating convincing human facial animation is challenging. Face animation is often hand-crafted by artists separately from body motion. Alternatively, if the face animation is derived from motion capture, it is typically performed while the actor is relatively still. Recombining the isolated face animation with body motion is non-trivial and often results in uncanny results if the body dynamics are not properly reflected on the face (e.g. cheeks wiggling when running). In this talk, I will discuss the challenges of human soft tissue simulation and control. I will then present our method for adding physical effects to facial blendshape animation. Unlike previous methods that try to add physics to face rigs, our method can combine facial animation and rigid body motion consistently while preserving the original animation as closely as possible. Our novel simulation framework uses the original animation as per-frame rest-poses without adding spurious forces. We also propose the concept of blendmaterials to give artists an intuitive means to control the changing material properties due to muscle activation.

Organizers: Timo Bolkart


Metrics Matter, Examples from Binary and Multilabel Classification

IS Colloquium
  • 21 August 2017 • 11:15 12:15
  • Sanmi Koyejo
  • Empirical Inference meeting room (MPI-IS building, 4th floor)

Performance metrics are a key component of machine learning systems, and are ideally constructed to reflect real world tradeoffs. In contrast, much of the literature simply focuses on algorithms for maximizing accuracy. With the increasing integration of machine learning into real systems, it is clear that accuracy is an insufficient measure of performance for many problems of interest. Unfortunately, unlike accuracy, many real world performance metrics are non-decomposable i.e. cannot be computed as a sum of losses for each instance. Thus, known algorithms and associated analysis are not trivially extended, and direct approaches require expensive combinatorial optimization. I will outline recent results characterizing population optimal classifiers for large families of binary and multilabel classification metrics, including such nonlinear metrics as F-measure and Jaccard measure. Perhaps surprisingly, the prediction which maximizes the utility for a range of such metrics takes a simple form. This results in simple and scalable procedures for optimizing complex metrics in practice. I will also outline how the same analysis gives optimal procedures for selecting point estimates from complex posterior distributions for structured objects such as graphs. Joint work with Nagarajan Natarajan, Bowei Yan, Kai Zhong, Pradeep Ravikumar and Inderjit Dhillon.

Organizers: Mijung Park


Challenges of writing and maintaining programs for robots

Talk
  • 04 August 2017 • 11:30 12:45
  • Mirko Bordignon
  • AMD meeting

Writing and maintaining programs for robots poses some interesting challenges. It is hard to generalize them, as their targets are more than computing platforms. It can be deceptive to see them as input to output mappings, as interesting environments result in unpredictable inputs, and mixing reactive and deliberative behavior make intended outputs hard to define. Given the wide and fragmented landscape of components, from hardware to software, and the parties involved in providing and using them, integration is also a non-trivial aspect. The talk will illustrate the work ongoing at Fraunhofer IPA to tackle these challenges, how Open Source is its common trait, and how this translates into the industrial field thanks to the ROS-Industrial initiative.

Organizers: Vincent Berenz