Andrea Cini
@andreacini1994
🇨🇭/🇬🇧 SNSF Postdoc Fellow at @UniofOxford. Postdoc affiliate @UiTNorgesarktis 🇳🇴 and @USI_university 🇨🇭 - Time series and graphs
Big personal update 😊 I'm moving to @UniofOxford to work with @mmbronstein on my recently funded Swiss National Science Foundation project 🎉🎉 As part of the project, I'll also be collaborating with @FilippoMariaBi1 😃 Looking forward to exciting new research & collabs! 😎
🚨 ICML 2025 Paper 🚨 "On Measuring Long-Range Interactions in Graph Neural Networks" We formalize the long-range problem in GNNs: 💡Derive a principled range measure 🔧 Tools to assess models & benchmarks 🔬Critically assess LRGB 🧵 Thread below 👇 #ICML2025
I will be in Vancouver next week for #ICML2025! If you’re around, would love to catch up 😊 Also, if you’re interested in uncertainty quantification in time series forecasting, come say hi at poster E-1706 on Tuesday from 16:30 to 19:00!
Freshly accepted as a tutorial paper in ACM Computing Surveys! 🥳 Check out the updated version: dl.acm.org/doi/abs/10.114… cc @IvanMarisca @dan_zambon
📢 Happy to finally share our paper on graph deep learning for time series forecasting! This puts together what we've learned in the past few years using GNNs for TS processing, I hope you'll find it useful😃 W/ @IvanMarisca, @dan_zambon and Cesare 🔥 🔗arxiv.org/abs/2310.15978
🚀 We just released the code for our new #ICML2025 paper "Relational Conformal Prediction for Correlated Time Series" by @andreacini1994 and co-authors! 💻 code: github.com/andreacini/cor… 📑 paper: arxiv.org/abs/2502.09443
📢 To align with other workshops, we extend the submission deadline to June 12th AoE! 🔗 Portal: openreview.net/group?id=rl-co… 🌐 More information at: sites.google.com/view/ibrl-work… 🚀 Looking forward to seeing you at @RL_Conference!
Introducing our 3rd Keynote 🎤 Ghada Sokar, Research Scientist at Google DeepMind and Adjunct Professor at Tu/e, will speak on "Network Plasticity and Scalability in Deep Reinforcement Learning" — exploring model adaptation and continuous learning under non-stationary conditions
💡deadline extension for our Temporal Graph Learning workshop: new deadline is May 25th, AOE 💡 looking forward to your submissions!
Submission deadline for our @kdd_news workshop is in 6 days, on May 20th AOE.😌 Topics include: Frontiers, Applications, Theory, Models, Methods and Evaluation for learning on temporal graphs! Position papers, extended abstracts and standard papers. The venue is non-archival.
Happy to share that CoRel has been accepted at ICML!🥳 CoRel quantifies uncertainty in correlated time series forecasting by leveraging graph deep learning operators. Check it out! 😎 W/ @alj_jenkins, Danilo, Cesare, and @FilippoMariaBi1 🔗 arxiv.org/abs/2502.09443
🚨 15 days left! 🚨 Don't miss the Call for Papers for the 3rd Temporal Graph Learning Workshop at KDD 2025 @kdd_news 📅 Deadline: May 20 (OpenReview) 🔗sites.google.com/view/tgl-works…
📢 Announcing the third edition of Temporal Graph Learning Workshop at KDD 2025 📢 📜 Call for Papers: Submit your work on Temporal Graphs, Evolving Relational Data, Spatio-Temporal Graphs & more! 📅 Submission Deadline: May 20 AoE (Openreview)
📣Great News 🎉 Our Workshop on Inductive Biases in Reinforcement Learning (IBRL) @ibrlworkshop has been accepted @RL_Conference (RLC), 2025. Interested in submitting your paper or participating as a reviewer? Check our website for more details: sites.google.com/view/ibrl-work…
✅ Works accepted at other venues are welcome if published after 1 September 2024 🔗 OpenReview portal: openreview.net/group?id=rl-co… 📝 If you want to participate as a reviewer, check: docs.google.com/forms/d/e/1FAI… 🌐 For more information, check: sites.google.com/view/ibrl-work…
📢 The Call for Papers for the IBRL workshop is now open! 🚀 🤖 Topics: abstractions and structured policies, generalization, relational biases and representations, biases for robotics, future directions, and more 🗓️ Submission Deadline: 30th May 2025, AoE 📙 Format: 4-8 pages
📢 Announcing the third edition of Temporal Graph Learning Workshop at KDD 2025 📢 📜 Call for Papers: Submit your work on Temporal Graphs, Evolving Relational Data, Spatio-Temporal Graphs & more! 📅 Submission Deadline: May 20 AoE (Openreview)
🔬 A superb collaboration with @andreacini1994 and Prof. Alippi (@USI_en), @jobrkr, @arunsau_, @DrFuSiongNg and Prof. Mandic (@imperialcollege, @CardiacEPAI, AI4Health Center) and collaborators at @UniofOxford ❤️ 📄Preprint: arxiv.org/abs/2502.09473 5/5
Just published a new blog on 🎱 Pooling in #GraphNeuralNetworks! 💡Learn the fundamentals through this gentle introduction and discover how they can improve your GNN applications. 👉 Check it out here: gnn-pooling.notion.site #GNN #MachineLearning #AI
Happy to share that our paper on feudal graph RL, led by @tommaso_marzi, has been accepted at TMLR! 🎉 Feudal graph RL is a new paradigm for designing hierarchical RL agents by relying on hierarchical GNNs. Check it out! 😉 Link: openreview.net/forum?id=wFcyJ…
Had an amazing time presenting the tutorial at the @LogConference with @IvanMarisca and @dan_zambon! 🚀 Looking forward to round 2 at the Italy meetup in Siena! 😊 Tutorial page: gmlg.ch/tutorials/grap…
Can't wait! 🔥
3. Graph Deep Learning for Time Series Processing: Forecasting, Reconstruction, and Analysis by @andreacini1994, @IvanMarisca, @dan_zambon 4. Integrating Knowledge Graphs and Large Language Models for Advancing Scientific Research by @qzhang_cs, @ChenJiaoyan1, @mengzaiqiao
🤔 How to interpret spatio-temporal data and deep learning models? 💡In our recent work with Michele Guerra and @s_scardapane we leverage Koopman theory to design an XAI framework for spatio-temporal GNNs. 📄 Preprint: arxiv.org/abs/2410.13469 💻 Code: github.com/NGMLGroup/Koop…