Very excited to present our "Differentiable Constraint-Based Causal Discovery" at #NeurIPS2025!
We introduce Differentiable d-Separation, bringing differentiable learning to Constraint-Based Causal Discovery.
Come to chat and learn more!
๐ Hall C,D,E #2407 ๐ Fri 11am-2pm PST
Today @arcinstitute releases State, our first perturbation prediction AI model and an important step towards our goal of a virtual cell
State is designed to learn how to shift cells between states (e.g. โdiseasedโ to โhealthyโ) using drugs, cytokines, or genetic perturbations
๐ Paper: https://t.co/RXpe3rLCkU
Come by our poster Friday morning #ICLR2025 and chat with @Josh_d_robinson about symmetries and task-agnostic representationsโจ
8/8
๐จ New paper at #ICLR2025!
๐งตIntroducing Holographic Node Representations, pretrained general-purpose node embeddings that adapt to any graph task (node, link, high-order)
๐Fri 10โฏAM SGT Hall 3 + 2B #189 presented by @Josh_d_robinson
w/ @Josh_d_robinson@jure@brunofmr
1/8
We also show that HoloGNN can recover canonical eigenvectors (๐ฐ๐ข๐ญ๐ก๐จ๐ฎ๐ญ ๐ฌ๐ข๐ ๐ง/๐๐๐ฌ๐ข๐ฌ ๐๐ฆ๐๐ข๐ ๐ฎ๐ข๐ญ๐ฒ) and use them in downstream tasks, outperforming expensive spectral methods!
7/8
If you are at #NeurIPS2024, donโt miss Moshe Eliasof presenting our ๐๐๐๐๐๐๐: ๐๐๐๐ฉ๐ญ๐ข๐ฏ๐ ๐๐จ๐ซ๐ฆ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐๐จ๐ซ ๐๐ซ๐๐ฉ๐ก ๐๐๐ฎ๐ซ๐๐ฅ ๐๐๐ญ๐ฐ๐จ๐ซ๐ค๐ฌ!
๐ Today 11 am
๐ East Exhibit #3000
Joint work w/ @caromitreka@HaggaiMaron
@caromitreka@HaggaiMaron TLDR: Learn different normalization parameters for every pair of non-isomorphic nodes
How? With an expressive (but scalable!) normalization GNN
Advantages: Adaptivity to the input graph, which results in consistent performance & faster convergence!
๐ฃStop by our @NeurIPSConf Poster #3103 Today at 11 AM! for DiGRAF: Diffeomorphic Graph Adaptive Activation Function - an activation function that adapts to the graph structure by learning a diffeomorphism.
Joint Work with Xinzhi Wang (equal contrib.), @caromitreka, @brunofmr , @beabevi_ , and Moshe Eliasof. ๐งต
Do you want to know how to learn to select a ๐ฌ๐ฆ๐๐ฅ๐ฅ ๐ฌ๐ฎ๐๐ฌ๐๐ญ ๐จ๐ ๐ฌ๐ฎ๐๐ ๐ซ๐๐ฉ๐ก๐ฌ for Subgraph GNNs?
Stop by our @iclrconf ๐๐จ๐ฌ๐ญ๐๐ซ #๐๐ ๐๐จ๐๐๐ฒ ๐๐ญ ๐๐:๐๐ to find out!
w/ @brunofmr@HaggaiMaron - https://t.co/njKbwbIB1Q
#ICLR2024
๐ป๐ธ๏ธToday at #ICML2023, @beabevi_ will present her internship work @GoogleDeepMind; I'll also be there!
We design a causality objective capturing a simple idea: many inputs to a target algorithm will induce identical initial behaviours.
2--3:30pm; Poster #227. See you there! ๐
๐ง๐ผ๐ฑ๐ฎ๐ ๐ฎ๐ ๐ฐ๐ฃ๐ ๐ถ๐ป ๐๐ฎ๐น๐น ๐ #๐ต๐ฏ๐ต, @ffabffrasca and I will be presenting โUnderstanding and Extending Subgraph GNNs by Rethinking their Symmetriesโ (#NeurIPS2022 Oral)!
Come chat w/ us about Subgraph GNNs, expressivity, and symmetries!
w/ @HaggaiMaron@mmbronstein