Ramon Viñas Torné — accepted AI-conference papers

2026

PACER: Acyclic Causal Discovery from Large-scale Interventional Data

Ramon Viñas Torné, Sílvia Fàbregas Salazar, Soyon Park, Ivo Alexander Ban, Artyom Gadetsky, Nikita Doikov +1

ICML 2026poster

Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensional settings where large-scale interventional data are increasingly available. While interventional data can substantially improve identifiability, exis…

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2025

gRNAde: Geometric Deep Learning for 3D RNA inverse design

Chaitanya K. Joshi, Arian Rokkum Jamasb, Ramon Viñas Torné, Charles Harris, Simon V Mathis, Alex Morehead +2

ICLR 2025spotlight

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequen…

Cited by 21 Source ↗PDF ↗code ↗Scholar ↗

2022

Attentional Meta-learners for Few-shot Polythetic Classification

Ben J Day, Ramon Viñas Torné, Nikola Simidjievski, Pietro Lió

ICML 2022spotlight

Polythetic classifications, based on shared patterns of features that need neither be universal nor constant among members of a class, are common in the natural world and greatly outnumber monothetic classifications over a set of features. We show that threshold meta-learners, such as Prototypical N…

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2022

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks

Arian Rokkum Jamasb, Ramon Viñas Torné, Eric J Ma, Yuanqi Du, Charles Harris, Kexin Huang +3

NeurIPS 2022accept

Geometric deep learning has broad applications in biology, a domain where relational structure in data is often intrinsic to modelling the underlying phenomena. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a…

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