Invited Talks

Since its inception as a field of study roughly one decade ago, research in algorithmic fairness has exploded. Much of this work focuses on so-called "group fairness" notions, which address the relative treatment of different demographic groups. More theoretical work advocated for "individual fairness" which, speaking intuitively, requires that people who are similar, with respect to a given classification task, should be treated similarly by classifiers for that task. Both approaches face significant challenges: for example, provable incompatibility of natural fairness desiderata (for the group case), and the absence of similarity information (for the individual case).

The past two years have seen exciting developments on several fronts in theoretical computer science including: the investigation of scoring, classifying, ranking, and auditing fairness under definitions aiming to bridge the group and individual notions, and the construction of similarity metrics from (relatively few) queries to a human expert. A parallel vein of research in the ML community explores fairness via representations. This talk will motivate, highlight, and weave together threads from these recent contributions.

Current approaches to AI and machine learning are still fundamentally limited in comparison with autonomous learning capabilities of children. What is remarkable is not that some children become world champions in certains games or specialties: it is rather their autonomy, flexibility and efficiency at learning many everyday skills under strongly limited resources of time, computation and energy. And they do not need the intervention of an engineer for each new task (e.g. they do not need someone to provide a new task specific reward function).

I will present a research program that has focused on computational modeling of child development and learning mechanisms in the last decade. I will discuss several developmental forces that guide exploration in large real world spaces, starting from the perspective of how algorithmic models can help us understand better how they work in humans, and in return how this opens new approaches to autonomous machine learning.

In particular, I will discuss models of curiosity-driven autonomous learning, enabling machines to sample and explore their own goals and their own learning strategies, self-organizing a learning curriculum without any external reward or supervision.

I will show how this has helped scientists understand better aspects of human development such as the emergence of developmental transitions between object manipulation, tool use and speech. I will also show how the use of real robotic platforms for evaluating these models has led to highly efficient unsupervised learning methods, enabling robots to discover and learn multiple skills in high-dimensions in a handful of hours. I will discuss how these techniques are now being integrated with modern deep learning methods.

Finally, I will show how these models and techniques can be successfully applied in the domain of educational technologies, enabling to personalize sequences of exercises for human learners, while maximizing both learning efficiency and intrinsic motivation. I will illustrate this with a large-scale experiment recently performed in primary schools, enabling children of all levels to improve their skills and motivation in learning aspects of mathematics. Web: http://www.pyoudeyer.com

Human language use is exquisitely sensitive to and grounded in perceptual context. I will describe how the "reference game" paradigm allows us to explore these aspects of language use, and to elicit grounded, contextual language for training neural language models. The resulting models predict human language use with high quantitative accuracy. For humans, language is more than a means of communication, it is also the way we distribute the learning problem across generations. I will describe how "concept learning games" allow us to explore this cultural knowledge transmission process and build models that learn from language (somewhat) as people do.

In Spike Jonze's futuristic film "Her", Theodore, a lonely writer, forms a strong emotional bond with Samantha, an operating system designed to meet his every need. Samantha can carry on seamless conversations with Theodore, exhibits a perfect command of language, and is able to take on complex tasks. She filters his emails for importance, allowing him to deal with information overload, she proactively arranges the publication of Theodore's letters, and is able to give advice using common sense and reasoning skills.

In this talk I will present an overview of recent progress on learning natural language interfaces which might not be as clever as Samantha but nevertheless allow uses to interact with various devices and services using every day language. I will address the structured prediction problem of mapping natural language utterances onto machine-interpretable representations and outline the various challenges it poses. For example, the fact that the translation of natural language to formal language is highly non-isomorphic, data for model training is scarce, and natural language can express the same information need in many different ways. I will describe a general modeling framework based on neural networks which tackles these challenges and improves the robustness of natural language interfaces.

Until about 2013, most researchers studying machine learning for artificial intelligence all worked on a common goal: get machine learning to work for AI-scale tasks. Now that supervised learning works, there is a Cambrian explosion of new research directions: making machine learning secure, making machine learning private, getting machine learning to work for new tasks, reducing the dependence on large amounts of labeled data, and so on. In this talk I survey how adversarial techniques in machine learning are involved in several of these new research frontiers.

Our planet is under ever increasing pressure from environmental degradation, biodiversity loss and climate change. If as a global society we are to respond to this we need to quantitatively assess the current state and future trends. Today, the planet’s vital signs are monitored extensively from space and from networks of ground-based sensors. In addition, over the past fifty years we have developed sophisticated numerical models of the Earth’s systems based on our understanding of physics, chemistry and biology. However, we are still limited in our ability to accurately predict future change, especially at the local scale that is most relevant for decision-making. In this talk I will outline a set of "grand challenge" problems and discuss various ways in which Machine Learning is starting to be deployed to advance our capacity to address these, in particular by combining our fundamental understanding of the Earth system processes with new knowledge gleaned from the vast planetary datasets.

Learning algorithms often capture spurious correlations present in the training data distribution instead of addressing the task of interest. Such spurious correlations occur because the data collection process is subject to uncontrolled confounding biases. Suppose however that we have access to multiple datasets exemplifying the same concept but whose distributions exhibit different biases. Can we learn something that is common across all these distributions, while ignoring the spurious ways in which they differ? This can be achieved by projecting the data into a representation space that satisfy a causal invariance criterion. This idea differs in important ways from previous work on statistical robustness or adversarial objectives. Similar to recent work on invariant feature selection, this is about discovering the actual mechanism underlying the data instead of modeling its superficial statistics.