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
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
This talk draws three parallels between classical algebraic quadrature rules, that are exact for polynomials of low degree, and kernel (or Bayesian) quadrature rules: i) Computational efficiency. Construction of scalable multivariate algebraic quadrature rules is challenging whereas kernel quadrature necessitates solving a linear system of equations, quickly becoming computationally prohibitive. Fully symmetric sets and Smolyak sparse grids can be used to solve both problems. ii) Derivatives and optimal rules. Algebraic degree of a Gaussian quadrature rule cannot be improved by adding derivative evaluations of the integrand. This holds for optimal kernel quadrature rules in the sense that derivatives are of no help in minimising the worst-case error (or posterior integral variance). iii) Positivity of the weights. Essentially as a consequence of the preceding property, both the Gaussian and optimal kernel quadrature rules have positive weights (i.e., they are positive linear functionals).
Organizers: Alexandra Gessner
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
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.
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.
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
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
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
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
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.