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Home/Authors/Mihaela van der Schaar

Mihaela van der Schaar

2 indexed papers

Recent (6 mo)
2
With code
0
Influential cites
0
Benchmarked
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Publications per year

2
26

Top categories

ML×2AI×2Stats ML×1

Frequent co-authors

Nicolas Huynh1×
Evgeny S. Saveliev1×
Samuel Holt1×
Nabeel Seedat1×
David L. Bentley1×
Jim Weatherall1×

Research Timeline

2026
Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

The paper introduces Influence-Guided Symbolic Regression (IGSR), a novel framework that uses granular influence scores to guide LLMs in efficiently searching for and discovering complex mathematical equations from scientific data.

Active Timepoint Selection for Learning Measure-Valued Trajectories

The paper proposes a novel active learning framework using Linearized Optimal Transport to strategically select measurement timepoints, thereby minimizing uncertainty when inferring continuous probability paths from sparse, high-dimensional biological data.

Highlighted terms show continued research focus across papers

Papers

cs.LGcs.AIstat.MLRecentMay 28, 2026

Active Timepoint Selection for Learning Measure-Valued Trajectories

Nicolas Huynh, Mihaela van der Schaar

The paper proposes a novel active learning framework using Linearized Optimal Transport to strategically select measurement timepoints, thereby minimizing uncertainty when inferring continuous probabi…

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cs.LGcs.AIRecentMay 27, 2026

Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback

Evgeny S. Saveliev, Samuel Holt, Nabeel Seedat, David L. Bentley +2 more

The paper introduces Influence-Guided Symbolic Regression (IGSR), a novel framework that uses granular influence scores to guide LLMs in efficiently searching for and discovering complex mathematical…

View →