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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.

Causal Macro Variables

IS Colloquium
  • 03 July 2017 • 11:15 12:15
  • Frederick Eberhardt
  • Max Planck House Lecture Hall

Standard methods of causal discovery take as input a statistical data set of measurements of well-defined causal variables. The goal is then to determine the causal relations among these variables. But how are these causal variables identified or constructed in the first place? Often we have sensor level data but assume that the relevant causal interactions occur at a higher scale of aggregation. Sometimes we only have aggregate measurements of causal interactions at a finer scale. I will motivate the general problem of causal discovery and present recent work on a framework and method for the construction and identification of causal macro-variables that ensures that the resulting causal variables have well-defined intervention distributions. Time permitting, I will show an application of this approach to large scale climate data, for which we were able to identify the macro-phenomenon of El Nino using an unsupervised method on micro-level measurements of the sea surface temperature and wind speeds over the equatorial Pacific.

Organizers: Sebastian Weichwald


Recent Projects on Lifelong Learning

Talk
  • 30 June 2017 • 15:30 16:45
  • Christoph Lampert
  • N2 Seminar Room

Organizers: Georg Martius


  • Sarah Bechtle
  • N2.025 (AMD seminar room - 2nd floor)

This work investigates the development of the sense of agency and of object permanence in humanoid robots. Based on findings from developmental psychology and from neuroscience, development of sense of object permanence is linked to development of sense of agency and to processes of internal simulation of sensor activity. In the course of the work, two sets of experiments will be presented, in the first set a humanoid robot has to learn the forward relationship between its movements and their sensory consequences perceived from the visual input. In particular, a self-monitoring mechanism was implemented that allows the robot to distinguish between self-generated movements and those generated by external events. In a second experiment, once having learned this mapping, the self-monitoring mechanism is exploited to suppress the predicted visual consequences of intended movements. The speculation is made that this process can allow for the development of sense of object permanence. It will be shown, that using these predictions, the robot maintains an enhanced simulated image where an object occluded by the movement of the robot arm is still visible, due to sensory attenuation processes.

Organizers: Stefan Schaal Lidia Pavel


  • Omur Arslan
  • N2.025 (AMD seminar room - 2nd floor)

In robotics, it is often practically and theoretically convenient to design motion planners for approximate simple robot and environment models first, and then adapt such reference planners to more accurate complex settings. In this talk, I will introduce a new approach to extend the applicability of motion planners of simple settings to more complex settings using reference governors. Reference governors are add-on control schemes for closed-loop dynamical systems to enforce constraint satisfaction while maintaining stability, and offers a systematic way of separating the issues of stability and constraint enforcement. I will demonstrate example applications of reference governors for sensor-based navigation in environments cluttered with convex obstacles and for smooth extensions of low-order (e.g., position- or velocity-controlled) feedback motion planners to high-order (e.g., force/torque controlled) robot models, while retaining stability and collision avoidance properties.

Organizers: Stefan Schaal Lidia Pavel


  • Seong Joon Oh
  • Aquarium

Growth of the internet and social media has spurred the sharing and dissemination of personal data at large scale. At the same time, recent developments in computer vision has enabled unseen effectiveness and efficiency in automated recognition. It is clear that visual data contains private information that can be mined, yet the privacy implications of sharing such data have been less studied in computer vision community. In the talk, I will present some key results from our study of the implications of the development of computer vision on the identifiability in social media, and an analysis of existing and new anonymisation techniques. In particular, we show that adversarial image perturbations (AIP) introduce human invisible perturbations on the input image that effectively misleads a recogniser. They are far more aesthetic and effective compared to e.g. face blurring. The core limitation, however, is that AIPs are usually generated against specific target recogniser(s), and it is hard to guarantee the performance against uncertain, potentially adaptive recognisers. As a first step towards dealing with the uncertainty, we have introduced a game theoretical framework to obtain the user’s privacy guarantee independent of the randomly chosen recogniser (within some fixed set).

Organizers: Siyu Tang


  • Matthias Niessner
  • PS Seminar Room (N3.022)

In the recent years, commodity 3D sensors have become easily and widely available. These advances in sensing technology have spawned significant interest in using captured 3D data for mapping and semantic understanding of 3D environments. In this talk, I will give an overview of our latest research in the context of 3D reconstruction of indoor environments. I will further talk about the use of 3D data in the context of modern machine learning techniques. Specifically, I will highlight the importance of training data, and how can we efficiently obtain labeled and self-supervised ground truth training datasets from captured 3D content. Finally, I will show a selection of state-of-the-art deep learning approaches, including discriminative semantic labeling of 3D scenes and generative reconstruction techniques.

Organizers: Despoina Paschalidou


  • Felix Leibfried and Jordi Grau-Moya
  • N 4.022 (Seminar Room EI-Dept.)

Autonomous systems rely on learning from experience to automatically refine their strategy and adapt to their environment, and thereby have huge advantages over traditional hand engineered systems. At PROWLER.io we use reinforcement learning (RL) for sequential decision making under uncertainty to develop intelligent agents capable of acting in dynamic and unknown environments. In this talk we first give a general overview of the goals and the research conducted at PROWLER.io. Then, we will talk about two specific research topics. The first is Information-Theoretic Model Uncertainty which deals with the problem of making robust decisions that take into account unspecified models of the environment. The second is Deep Model-Based Reinforcement Learning which deals with the problem of learning the transition and the reward function of a Markov Decision Process in order to use it for data-efficient learning.

Organizers: Michel Besserve


Bayesian Probabilistic Numerical Methods

Talk
  • 13 June 2017 • 11:00 12:00
  • Jon Cockayne

The emergent field of probabilistic numerics has thus far lacked rigorous statistical foundations. We establish that a class of Bayesian probabilistic numerical methods can be cast as the solution to certain non-standard Bayesian inverse problems. This allows us to establish general conditions under which Bayesian probabilistic numerical methods are well-defined, encompassing both non-linear models and non-Gaussian prior distributions. For general computation, a numerical approximation scheme is developed and its asymptotic convergence is established. The theoretical development is then extended to pipelines of numerical computation, wherein several probabilistic numerical methods are composed to perform more challenging numerical tasks. The contribution highlights an important research frontier at the interface of numerical analysis and uncertainty quantification, with some illustrative applications presented.

Organizers: Michael Schober


  • Alexey Dosovitskiy
  • PS Seminar Room (N3.022)

Our world is dynamic and three-dimensional. Understanding the 3D layout of scenes and the motion of objects is crucial for successfully operating in such an environment. I will talk about two lines of recent research in this direction. One is on end-to-end learning of motion and 3D structure: optical flow estimation, binocular and monocular stereo, direct generation of large volumes with convolutional networks. The other is on sensorimotor control in immersive three-dimensional environments, learned from experience or from demonstration.

Organizers: Lars Mescheder Aseem Behl


  • Alexey Dosovitskiy
  • PS Seminar Room (N3.022)

Our world is dynamic and three-dimensional. Understanding the 3D layout of scenes and the motion of objects is crucial for successfully operating in such an environment. I will talk about two lines of recent research in this direction. One is on end-to-end learning of motion and 3D structure: optical flow estimation, binocular and monocular stereo, direct generation of large volumes with convolutional networks. The other is on sensorimotor control in immersive three-dimensional environments, learned from experience or from demonstration.

Organizers: Lars Mescheder Aseem Behl