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A few weeks ago, the biggest European conference on machine learning was held: ECML 2020. Our research engineer Nourchène, our R&D consultant Gianmarco, and our data scientist Ronan attended the event from Tunisia, Belgium and Marseille. In this article, they tell you about the different keynote talks they attended.
Gemma Galdon-Clavell – Algorithmic Auditing: how to open the black-box of ML
Nourchène says: “I loved the talk given by Gemma Galdon-Clavell during which she addressed the problem of ethics in AI, as computer science engineers do not often question what they are producing from a moral standpoint. In her talk, Gemma points out the importance of data used to train a machine learning model. Data are provided by humans, but people are not perfect, they are likely to make wrong decisions. The model will then learn to behave the same way. So we might end up creating an unethical model. This can lead to two different behaviours: users either will follow the system’s recommendations at any cost or decide not to if they find the decisions not reasonable. Data will then continue to be biased, which creates a sort of deadlock.”
Ronan adds: “Algorithms do not produce biases from anywhere; they reproduce and amplify biases they can find in the data they ingest. As a result, we have to pay attention first to the quality of the data we use. Gemma emphasizes that algorithmic auditing is the key to understanding if the algorithm meets the expectations and if it complies with the regulations. The audit does not only cover the technical part and the way the algorithm was coded. It also focuses on how the problem was approached and the means deployed to solve it.”
Nourchène explains: “The speaker suggests that before creating a product, computer science engineers and developers need to ask the following questions: Is the product desirable and what is the problem that it tries to solve? Is it acceptable and does it involve users? Is it legal? Finally, does it use the right data? Gemma also suggests that ethics be taught in engineering schools. I totally agree with that because nowadays technology does not always seek to solve real problems, its goal is rather to make a fortune out of the proposed product.”
Max Welling – Amortized and Neural Augmented Inference
Gianmarco says: ‘My favourite talk was the one held by Max Welling. It clearly showed and unified the underlying theoretical grounds of many superficially different models, without failing to provide real-world applications. More concretely, the talk showed how to develop hybrid amortized methods that combine classical learning, inference and optimization algorithms with learned neural networks, which is of strong interest, especially in physics-related fields.
It provided a comprehensive and complete exposition of the topic of amortized neural inference and, as a consequence, it did not fail in bringing the spectator up-to-date with applications in that regard. Max Welling presented how a learned neural network can augment or correct a classical solution (attained by means of expert-knowledge or classical equations), or reversely, how a neural network can be fed useful information computed by a classical method.”
Been Kim – Interpretability for everyone
Gianmarco says: “I was exposed to many new topics and applications I was not familiar with. Talks like Interpretability for everyone that offered more abstract research were the ones that struck my attention the most. The talk presented the latest discoveries and tools in terms of interpretability quantification. It also introduces how to extract interpretability from a black-box end-to-end model, which I find very important for the construction of more robust models and model diagnosis.”
Doina Precup – Building Knowledge For AI Agents With Reinforcement Learning
Ronan says: “I really liked the talk given by Doina Precup on how to build knowledge in the field of reinforcement learning. I only had little knowledge of this field. Thankfully, Doina introduced us quickly to the key concepts of reinforcement learning. She also presented us with some big successes of RL, presented different RL mechanisms and went towards the problem of using existing knowledge to build a life-long learning agent. Doina concluded her talk with a lot of open and inspiring questions: How can we exploit previously learned knowledge and apply it to new environments not related in any manner to the previous ones? How well is an agent preserving and enhancing its knowledge? These questions might not have definitive answers or just answers at all but I found very relevant and interesting the interrogations she raises on how we can represent knowledge.
Stephan Günnemann about Certifiable Robustness of ML Models for Graphs
Ronan says: In this technical talk, Stephan presented us different methods to assess GNN robustness. To certificate the robustness of a GNN, an evaluation of its sensitivity to perturbations needs to be conducted. For example, you can search for a worst-case scenario, and verify that the margin is positive to ensure the model is robust. Stephan’s talk was very pleasant to listen to, as he accompanied it with several examples and applications of the methods he presented us. Finally, he concluded that ML models for graphs aren’t reliable but that we can apply certificates and robustification principles to provide guarantees for a reliable use of GNNs.
Watch the talks:
If you wish to catch up on talks we mentioned or those you missed, all the sessions, paper and presentation recordings are available (for a limited time) from the ECML website.
Max Welling :