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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. What were the big trends and their favourite talks? What did they think of the online remote format? Let’s find out with them!
The overall conference was very well up-to-date with the outside world’s latest trends and needs. Gianmarco explains: “The conference was rich in presentations which covered nearly all possible topics in machine learning. However, I had the impression that Graph Neural Networks and Generative Models had a little more presence than other models. Transfer learning was also another topic that seemed to be very relevant throughout the conference.”
Due to the COVID-19 pandemic, the conference was fully virtual. The talks were pre-recorded and made available prior to the conference. The live sessions were dedicated to questions and answers, with a very brief presentation at the beginning of the session.
Nourchène explains: “The downside was that we had to watch the whole presentation beforehand, otherwise it was difficult to follow the discussion and to interact with the speaker. Fun fact: there was a session where even the moderator was not aware of this Q&A aspect and asked the speaker why the presentation was so short! The good thing is that, since the presentations were pre-recorded, it was possible to watch the presentations from sessions running in parallel.”
Gianmarco adds: “I have not had many remote conferences in my life, but I was genuinely surprised to see how well-organised this one was. The remote framework was very well-designed, the web interface was fully functional, and they took advantage of all the benefits that a remote event can have like re-watchable presentations.”
Kudos to the organising committee for pulling it off!
We wrote an article with more details about different keynotes that you can find on this link, but here is a teaser:
Gemma Galdon-Clavell – Algorithmic Auditing: how to open the black-box of ML
In her talk, Gemma points out the importance of data used to train a machine learning model. According to her, algorithmic auditing is the key to understanding if the algorithm meets the expectations and if it complies with the regulations. This 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. Read our detailed review here.
Max Welling – Amortized and Neural Augmented Inference
The talk showed and unified the underlying theoretical grounds of many superficially different models, without failing to provide real-world applications. It provides 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. Read more here.
Been Kim – Interpretability for everyone
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. Read more in our article.
Doina Precup – Building Knowledge For AI Agents With Reinforcement Learning
Doina Precup talks on how to build knowledge in the field of reinforcement learning. She also presents 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. Discover more!
Stephan Günnemann – Certifiable Robustness of ML Models for Graphs
Stephan presented different methods to assess GNN robustness: an evaluation of its sensitivity to perturbations needs to be conducted. Learn more with Ronan here.
Si-An Chen; Voot Tangkaratt; Hsuan-Tien Lin; Masashi Sugiyama – Active deep Q-learning with demonstration
Nourchène says: “The authors presented their paper proposing different groups of techniques for learning from demonstration in Reinforcement Learning, like RL Expert Demonstration (RLED) or Active RL Demonstration (ARLD). These techniques can be used to fasten the learning process of an RL agent. They also propose an uncertainty-based query strategy named Active Deep Q-Network, based on DQN, to dynamically estimate the uncertainty of recent states and use the queried demonstration data.“
Learning With Imbalanced Domains and Rare Event Detection
Ronan says: “This tutorial was interesting and well-structured. Imbalance domains and rare-events prediction concern a lot of domains: financial, medical, data distribution… and will always remain a centre of attention in designing the appropriate solution to a problem. As a consequence, it will remain a core problem in the research. I particularly liked this tutorial as it covered a lot of different approaches: unsupervised (statistical-based, proximity-based, clustering-based), supervised and semi-supervised and compared them. As there is no ideal solution that can be applied to every problem, you have to know what exists before choosing the one that better fits your problem. The tutorial also covered different methods to properly evaluate the performance of an algorithm on an imbalanced task. ”
The conference provided a wide range of machine learning topics in the form of presentations about the latest trends, technologies and applications. As Nourchène says: “it is an optimal platform to stay up-to-date, to widen one’s perspectives and/or dig deeper into a specific topic.”
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.
Active deep Q-learning with demonstration: Read the paper