van der Schaar Lab @ ICML 2026

We are delighted to share that 13 papers from the van der Schaar Lab have been accepted to the forty-third International Conference on Machine Learning (ICML). ICML is one of the world’s premier academic conferences dedicated to machine learning, and this year will be held in Seoul, South Korea from July 6th – 11th 2026.

This year, accepted works from the lab span a wide range of directions including AI agents, digital twins, scientific discovery, and causal inference, reflecting both the breadth and depth of research across the group. We are especially proud of the students and collaborators behind these projects, and we look forward to presenting this work in Korea later this year.

Over the coming weeks, we will be highlighting each paper individually, sharing more about the ideas, technical contributions, and researchers behind the work.

Congratulations to everyone involved.

The accepted papers are listed below:

• AgentScore: Autoformulation of Deployable Clinical Scoring Systems
  Silas Ruhrberg Estévez, Christopher Chiu, Mihaela van der Schaar
• DT^2: Decision-Targeted Digital Twins
  Harry Amad, Mihaela van der Schaar
• No More, No Less: Least-Privilege Language Models
  Paulius Rauba, Dominykas Seputis, Patrikas Vanagas, Mihaela van der Schaar
• Skill Neologisms: Towards Skill-based Continual Learning
  Antonin Berthon, Nicolás Astorga, Mihaela van der Schaar
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
  Evgeny Saveliev, Samuel Holt, Nabeel Seedat, David L. Bentley, Jim Weatherall, Mihaela van der Schaar
• Active Timepoint Selection for Learning Measure-Valued Trajectories
  Nicolas Huynh, Mihaela van der Schaar
• CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment
  Silas Ruhrberg Estévez, Nicolas Huynh, Tennison Liu, Roderik M. Kortlever, Gerard I. Evan, David L. Bentley, Mihaela van der Schaar
• When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets
  Christopher Chiu, Simpson Zhang, Mihaela van der Schaar
• Position: The AI Imperative: Scaling High-Quality Peer Review in Machine Learning
  Qiyao Wei, Samuel Holt, Jing Yang, Markus Wulfmeier, Mihaela van der Schaar
• Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification
  Yichi Zhang, Nabeel Seedat, Yinpeng Dong, Peng Cui, Jun Zhu, Mihaela van der Schaar
• Gradient-Based Causal Tree Ensembles: A Backbone Architecture for Heterogeneous Treatment Effects
  Yusuke Kano, Jeremy Paul Voisey, Mihaela van der Schaar
• Identifiable Nonlinear Differentiable Causal Discovery via Independence and Adaptive Group Sparsity
  Ruicong Yao, Tim Verdonck, Mihaela van der Schaar, Jakob Raymaekers
• Nonparametric LLM Evaluation from Preference Data
  Dennis Frauen, Athiya Deviyani, Mihaela van der Schaar, Stefan Feuerriegel