Deep Learning has become an essential toolbox which is used in a wide variety of applications, research labs, industry, etc. In this tutorial, we will provide a set of guidelines which will help newcomers to the field understand the most recent and advanced models, their application to diverse data modalities (such as images, videos, waveforms, sequences, graphs,) and to complex tasks (such as learning to learn from a few examples, or generating molecules).

Optimal transport (OT) provides a powerful and flexible way to compare probability measures, discrete and continuous, which includes therefore point clouds, histograms, datasets, parametric and generative models. Originally proposed in the eighteenth century, this theory later led to Nobel Prizes for Koopmans and Kantorovich as well as Villani’s Fields Medal in 2010. OT recently has reached the machine learning community, because it can tackle challenging learning scenarios including dimensionality reduction, structured prediction problems that involve histogram outputs, and estimation of generative models such as GANs in highly degenerate, high-dimensional problems. Despite very recent successes bringing OT from theory to practice, OT remains challenging for the machine learning community because of its mathematical formality. This tutorial will introduce in an approachable way crucial theoretical, computational, algorithmic and practical aspects of OT needed for machine learning applications.

There has been recent very exciting advances in (deep) reinforcement learning, particularly in the areas of games and robotics. Yet perhaps the largest impact could come when reinforcement learning systems interact with people. In this tutorial we will discuss work on reinforcement learning for helping and assisting people, and frameworks and approaches for enabling people helping reinforcement learning. We will cover

Background on reinforcement learning. Reinforcement learning for people-focused applications Approaches for enabling people to assist reinforcement learners

A number of the ideas presented here will also be relevant to many high stakes reinforcement learning systems.

Target audience: The majority of the tutorial will be aimed at an audience who has a basic machine learning background (e.g. as acquired by a class or equivalent).

Learning objectives: Know some of the key technical challenges that arise for reinforcement learning in people-focusing domains; understand some of the algorithms and approaches that have been developed to address these challenges; become familiar with some of the other application areas that have also or can also benefit from reinforcement learning.

This tutorial will provide a gentle introduction into the foundations of statistical relational artificial intelligence, and will realize this by introducing the foundations of logic, of probability, of learning, and their respective combinations. Both predicate logic and probability theory extend propositional logic, one by adding relations, individuals and quantified variables, the other by allowing for measures over possible worlds and conditional queries. While logical and probabilistic approaches have often been studied and used independently within artificial intelligence, they are not in conflict with each other but they are synergistic. This explains why there has been a considerable body of research in combining first-order logic and probability over the last 25 years, evolving into what has come to be called Statistical Relational Artificial Intelligence (StarAI).

Relational probabilistic models — we use this term in the broad sense, meaning any models that combine relations and probabilities — form the basis of StarAI, and can be seen as combinations of probability and predicate calculus that allow for individuals and relations as well as probabilities. In building on top of relational models, StarAI goes far beyond reasoning, optimization, learning and acting optimally in terms of a fixed number of features or variables, as it …

Over the past few years, fairness has emerged as a matter of serious concern within machine learning. There is growing recognition that even models developed with the best of intentions may exhibit discriminatory biases, perpetuate inequality, or perform less well for historically disadvantaged groups. Considerable work is already underway within and outside machine learning to both characterize and address these problems. This tutorial will take a novel approach to parsing the topic, adopting three perspectives: statistics, causality, and measurement. Each viewpoint will shed light on different facets of the problem and help explain matters of continuing technical and normative debate. Rather than attempting to resolve questions of fairness within a single technical framework, the tutorial aims to equip the audience with a coherent toolkit to critically examine the many ways that machine learning implicates fairness.

Neural network models are algorithmically simple, but mathematically complex. Gaussian process models are mathematically simple, but algorithmically complex. In this tutorial we will explore Deep Gaussian Process models. They bring advantages in their mathematical simplicity but are challenging in their algorithmic complexity. We will give an overview of Gaussian processes and highlight the algorithmic approximations that allow us to stack Gaussian process models: they are based on variational methods. In the last part of the tutorial will explore a use case exemplar: uncertainty quantification. We end with open questions.

Differential privacy has emerged as one of the de-facto standards for measuring privacy risk when performing computations on sensitive data and disseminating the results. Algorithms that guarantee differential privacy are randomized, which causes a loss in performance, or utility. Managing the privacy-utility tradeoff becomes easier with more data. Many machine learning algorithms can be made differentially private through the judicious introduction of randomization, usually through noise, within the computation. In this tutorial we will describe the basic framework of differential privacy, key mechanisms for guaranteeing privacy, and how to find differentially private approximations to several contemporary machine learning tools: convex optimization, Bayesian methods, and deep learning.

In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data, while many important applications have to deal with non-Euclidean structured data, such as graphs and manifolds. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, and web applications. The adoption of deep learning in these fields has been lagging behind until recently, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive.

The purpose of the proposed tutorial is to introduce the emerging field of geometric deep learning on graphs and manifolds, overview existing solutions and applications for this class of problems, as well as key difficulties and future research directions.

Recent successes in computer vision, natural language processing and other areas of artificial intelligence have been largely driven by methods for sophisticated pattern recognition — most prominently deep neural networks. But human intelligence is more than just pattern recognition. In particular, it depends on a suite of cognitive capacities for modeling the world: for making judgment calls in ambiguous situations, explaining and understanding what we see, imagining things we could see but haven’t yet, solving problems and planning actions to make these things real, and building new models as we learn more about the world. We will talk about prospects for reverse-engineering these capacities at the heart of human intelligence, and using what we learn to make machines smarter in more human-like ways. We introduce basic concepts and techniques of probabilistic programs, inference programming and program induction, which together with tools from deep learning and modern video game engines provide an approach to capturing many aspects of everyday intelligence.

Specific units in our tutorial will show how:

(1) Defining probabilistic programs over algorithms and representations drawn from modern video game engines — graphics engines, physics engines, and planning engines — allows us to capture how people can perceive rich three-dimensional …