Are you curious about how domain-specific knowledge can help shape AI applications within critical network infrastructures? We’re excited to invite you to our upcoming webinar, “Knowledge-Assisted AI Applications for Real-World Network Infrastructure”, where we’ll discuss potential applications of AI within the unique domains of the AI4REALNET project.
More information, and a link to the mandatory registration form, can be found here.
Do you like to investigate how reinforcement learning agents can optimally support human decision making? Frans Oliehoek and me are looking for a postdoc at the University of Amsterdam.
Directly apply, or find more information, here.
The position is funded by the AI4REALNET Project and The Hybrid Intelligence Centre.
Tomorrow, June 4th, David & Guillermo will present their work at ICAPS. The paper proposes a new way to learn sub-policies that can optimally solve complex tasks expressed in linear temporal logic, even in stochastic environments. They’d love to tell you all about it. Or read our paper here.
Together with Maryam Tavakol & Vincent Francois-Lavet, we are organizing the 2024 edition of the Belgian-Netherlands Reinforcement Learning Workshop (BeNeRL) in Amsterdam! It will take place on June 10th, and will be a free event thanks to generous support by the Ellis Unit Amsterdam and the NWO. Registration is required, though.
More info & sign-up at the event website.
After his postdoc, Masoud has recently started as assistant professor in the Multimedia Computing Group of TU Delft. Congratulations, Masoud!
“Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems” by Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Maarten de Rijke, and me, was accepted for presentation in SIGIR!
David & Guillermo’s paper “Planning with a Learned Policy Basis to Optimally Solve Complex Tasks” was accepted for presentation at ICAPS. This paper investigates how pre-trained sub-policies can be used to complete complex tasks described by reward machines, without losing optimality. Congrats!
Our paper on Uncoupled Learning of Differential Stackelberg Equilibria with Commitments has been accepted for publication at AAMAS. Congratulations, Robert and Mert!
Preprint is accessible here.
At Amlab, we have two open PhD positions on machine learning in the fintech domain (in collaboration with Adyen). One student will work with Sara Magliacane on causal machine learning, and the second student will work with me on reinforcement learning. Deadline for applications is March 11th. For all details and how to apply, please see the official vacancy.
We are looking for a PhD candidate to work on several fundamental questions necessary for allowing AI methods to support human operators in critical infrastructure. For example, can machine learning tools be combined with conventional optimisers to improve safety and data efficiency? Can complex, structured decisions be made jointly by a human operator and an artificial agent? Can such algorithms deal with hierarchies in decision making, and how can decisions or models be explained and verified? Full details and instructions to apply can be found on this page.