Our first keynote: Miguel Hernán CAUSALab on Causal AI for data analysis in the health sciences #causality #CleaR2023

🚨We have an open PhD position UvA AMLab Informatics Institute University of Amsterdam in #causality-inspired ML for #robust #fraud #detection with dr. Ana Mićković UvA Amsterdam Business School 🚨 This is part of the #ai4fintech program UvA Amsterdam, and in partnership with Adyen Deadline: 21 May 2023 vacatures.uva.nl/UvA/job/Seven-…

Delighted to be in Kigali for #ICLR2023! If you happened to have missed my poster, yet still want to link up on scaling multi-objective optimization to more objectives, feel free to hit me up here or on Whova. Happy to chat over coffee and pastries😋 openreview.net/forum?id=fSa5I…

🚨CFP #Causal #Representation #Learning NeurIPS Conference 2023 WS🚨 Do you have ideas on how to learn #causal representations from raw data, e.g. images, #abstract #macro variables or leverage #causality in #ML? Then submit a max 6 pg paper to our workshop crl-workshop.github.io

Are you at ICML Conference and interested in chatting about Clifford geometric algebras? Come by at our poster #534 from 11am onward!

Interested in deep learning for medical imaging inverse problems at scale? Join the Biomedical Imaging group at Microsoft Research to work on this beautiful project! jobs.careers.microsoft.com/global/en/job/…

We are excited to announce the newly created seminar series: 🚀Amsterdam Causality Meeting 🚀 📅 Monday October 9th, 15:00-18:00 📍 UvA Science Park room D1.114 🔗 amscausality.github.io Register for the Google Group here: [email protected]

With @stephanievdpas & Joris Mooij we are organizing a seminar series on #causality in Amsterdam: 4 events per year, each with 2 talks, rotating across the different institutions in AMS. First seminar is Oct 9 at UvA Amsterdam with Nan van Geloven (LUMC) & Sander Beckers (UvA) 1/2

We proudly present our 524 page book on equivariant convolutional networks. Coauthored by Patrick Forré, Erik Verlinde and Max Welling. maurice-weiler.gitlab.io/#cnn_book [1/N]

📣📣This is a friendly reminder for today’s Amsterdam Causality Meeting 🚀

Excited to share Rotating Features – accepted as oral at #NeurIPS2023 Rotating Features learn to represent object affiliation via their orientation on real-world data without labels. Let’s dive in! 📜 arxiv.org/abs/2306.00600 🖥️ github.com/loeweX/Rotatin…

AMLab will be presenting 8 papers at NeurIPS 2023, including 2 oral presentations! Please see our blog for the full list. 📰: amlab.science.uva.nl/blog/2023/neur…

🚨The Amsterdam Machine learning group UvA AMLab is hiring 2 PhDs on #AI4Fintech in collaboration with Adyen 🚨 - 1 PhD on #causalML with Sara Magliacane (she/her) - hiring 2 PhD students - 1 PhD on #RL with Herke van Hoof Deadline: **11 March 2024** Details: vacatures.uva.nl/UvA/job/2-PhD-…

[1/7] 🎉Our paper Time-lagged Information Bottleneck (T-IB) is in #ICLR2024! Kudos to Bas Veeling Ryota Tomioka Patrick Forré & MSR AI4Science! 📄arxiv.org/abs/2309.07200 T-IB maps complex dynamics to simple latent spaces for super fast, highly accurate simulations!

Excited to announce the 9th #causality workshop at UAI 2025 with Razieh Nabi Karthika Mohan Caleb Miles @d_malinsky 🎉 Do you have interesting work in #causality #causalInference #causalDiscovery #CRL etc? Consider submitting an abstract by May 31st: sites.google.com/view/causaluai…

Announcing our new model for materials! FlowMM... - Generates stable & novel materials efficiently - Predicts crystal structure accurately - Generalizes Riemannian Flow Matching to point clouds w/ periodic boundaries arxiv.org/abs/2406.04713 Ricky T. Q. Chen Anuroop Sriram Brandon Wood

📢 Now available: the first paper from my PhD is out in Neurobiology of Stress✨. We conducted an exhaustive behavioral phenotyping of the sex-dependent effects of ELS on fear memory. Thread below 👇🏻

Exciting UvA AMLab seminar by Luigi Gresele (Copenhagen Causality Lab) on "Representation Learning with Orthogonal Coordinate Transformations" today at UvA Amsterdam - what insights does #causality provide for generative models with provable guarantees?

Introducing Latent Program Network (LPN), a new architecture for inductive program synthesis that builds in test-time adaption by learning a latent space that can be used for search 🔎 Inspired by ARC Prize 🧩, we designed LPN to tackle out-of-distribution reasoning tasks!

Thrilled to see our NeurIPS 2024 paper, Sequential Monte Carlo Policy Optimisation (arxiv.org/abs/2402.07963), featured in Kevin's Reinforcement Learning: A Comprehensive Overview, which additionally recognises SMC as a competitive, scalable online planner. A fantastic modern