Videos of the 2019 edition

Distributed Machine Learning over Networks

Francis Bach, Professor at Inria and Ecole Normale Supérieure

The success of machine learning models is in part due to their capacity to train on large amounts of data. Distributed systems are the common way to process more data than one computer can store, but they can also be used to increase the pace at which models are trained by splitting the work among many computing nodes. In this talk, I will study the corresponding problem of minimizing a sum of functions which are respectively accessible by separate nodes in a network. New centralized and decentralized algorithms will be presented, together with their convergence guarantees in deterministic and stochastic convex settings, leading to optimal algorithms for this particular class of distributed optimization problems.

Knowledge representation and model-based image understanding

Isabelle Bloch, Professor at LTCI, Télécom Paris, Institut Polytechnique de Paris

In this talk, we will discuss the importance of knowledge and models to guide image understanding, and present a few examples. In these examples, structural information is expressed as mathematical models of spatial relations, using fuzzy sets and mathematical morphology. This knowledge is included in models such as ontologies, graphs, logical knowledge bases. Image understanding is then expressed as a spatial reasoning problem. Examples in medical imaging will illustrate these approaches. Finally, recent developments on transfer learning will be illustrated.

Learning Anonymized Representations with Mutual Information

Pablo Piantanida, Associate Professor of Information Theory at CentraleSupelec

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. In this talk, we present a statistical anonymization method based on representation learning and deep neural networks. Our approach employs adversarial networks to perform a novel variational approximation of the mutual information between the representations and the user’s identity. We introduce a training objective for simultaneously learning representations that preserve the information of interest (e.g., about regular labels) while dismissing information about the identity of a person (e.g., about private labels). We demonstrate the success of this approach for standard classification versus anonymization tasks.

Privacy-Preserving Algorithms for Decentralized Collaborative Machine Learning

Aurélien Bellet, Researcher at Inria Lille, MAGNET & CRISTAL Projet-Teams

With the advent of connected devices with computation and storage capabilities, it becomes possible to run machine learning on-device to provide personalized services. However, the currently dominant approach is to centralize data from all users on an external server for batch processing, sometimes without explicit consent from users and with little oversight. This centralization poses important privacy issues in applications involving sensitive data such as speech, medical records or geolocation logs.

Ethics and autonomous agents

Grégory Bonnet, Associate Professor, GREYC Lab, Normandie University

Abstract: Recent years have been marked by computer science achievements suggesting that artificial intelligence, robots or autonomous machines will invest more and more in our daily environment. As a result of interacting more and more with humans, an interest in designing moral or ethical autonomous agents has been raised. In this talk, I will investigate first what kind of ethical issues autonomous agents may raise, and what could be an ethical autonomous agent. Then I will present logical architectures that may be useful to design autonomous agents embedded with explicit ethical reasoning capabilities, such as attributing causality and responsibilities, judging, deciding and acting according to ethical principles.

Developmental Autonomous Learning: AI, Cognitive Sciences and Educational Technology

Pierre-Yves Oudeyer, Professor at Inria, Bordeaux University and ENSTA Paris

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).

Tackling the Data-Efficiency Challenge in Autonomous Robots Using Probabilistic Modeling

Marc Deisenroth, Senior Lecturer, Imperial College London

The vision of intelligent and fully autonomous robots, which are part of our daily lives and automatically learn from mistakes and adapt to new situations, has been around for many decades. However, this vision has been elusive so far. Although reinforcement learning is a principled framework for learning from trial and error and has led to success stories in the context of games, we need to address a practical challenge when it comes to learning with mechanical systems: data efficiency, i.e., the ability to learn from scarce data in complex domains.

Meta-learning as a Markov Decision Process

Lisheng Sun, PhD student at LRI, University Paris Sud

Machine Learning (ML) has enjoyed huge successes in recent years and an ever-growing number of real-world applications rely on it. However, designing promising algorithms for a specific problem still requires huge human effort. Automated Machine Learning (AutoML) aims at taking the human out of the loop and develop machines that generate / recommend good algorithms for a given ML tasks. AutoML is usually treated as a algorithm / hyper-parameter selection problems, existing approaches include Bayesian optimization, evolutionary algorithms as well as reinforcement learning.

Graph-to-Sequence Learning in Natural Language Processing

Lingfei Wu, Research Staff Member in the IBM AI Foundations Labs

The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, natural language generation, speech recognition, and drug discovery. Despite their flexibility and expressive power, a significant limitation with the Seq2Seq models is that a neural network can only be applied to problems whose inputs are represented as sequences. However, the sequences are probably the simplest structured data and many important problems are best expressed with a complex structure such as a graph. On one hand, these graph-structured data can encode complicated pairwise relationships for learning more informative representations; On the other hand, the structural and semantic information in sequence data can be exploited to augment original sequence data by incorporating the domain-specific knowledge.

When statistical physics meets machine learning

Lenka Zdeborová, CNRS Researcher, Institut de Physique Théorique – CEA

The affinity between statistical physics and machine learning has long history, this is reflected even in the machine learning terminology that is in part adopted from physics. The very purpose of physics is to provide understanding for empirically observed behaviour. From this point of view, the current success of machine learning provides a myriad of yet unexplained empirical observations that call for explanation. Physics functions by study of models that are simple enough to be studied and at the same time capture the salient features of the real system. In this lecture I will describe some of the history of statistical physics applied to machine learning and focus of the current hunt for suitable models, starting with a reflection on what should be the salient features they should capture, and methods to possibly solve them.

International Workshop on Machine Learning & Artificial Intelligence

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Session 1: Methodological Approaches of Machine Learning