ARXIV:2605.30625 · ACTIVE LEARNING · SUBMITTED 01 JUN · 20:24 UTC · FRESHNESS STALE

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Active Timepoint Selection for Learning Measure-Valued Trajectories

Nicolas Huynh · Mihaela van der Schaar · arXiv

An active learning framework uses Linearized Optimal Transport and Gaussian Processes to select optimal measurement times for inferring measure-valued trajectories from sparse data.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain An active learning framework uses Linearized Optimal Transport and Gaussian Processes to select optimal measurement times for inferring measure-valued trajectories from sparse data.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

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PROBLEM

An active learning framework uses Linearized Optimal Transport and Gaussian Processes to select optimal measurement times for inferring measure-valued trajectories from sparse data. This motivates the need for active learning strategies to strategically select…

METHOD

Full abstract

Inferring continuous probability paths from sparse snapshots is a fundamental challenge in domains like single-cell biology, where high-fidelity data acquisition is often destructive and constrained by prohibitive sequencing costs. This motivates the need for active learning strategies to strategically select optimal measurement times. However, designing active learning policies for this setting remains an open problem: the target objects reside on the infinite dimensional Wasserstein space where standard Euclidean metrics are ill-defined, and current interpolation methods lack epistemic uncertainty quantification. We introduce a framework which extends active experimentation to the space of measures. By leveraging Linearized Optimal Transport (LOT), we map distributional snapshots into a tangent space amenable to Gaussian Process modeling, allowing us to construct a tractable probabilistic surrogate for the underlying probability path. This yields an acquisition policy that iteratively selects measurement times to minimize uncertainty. Empirical results demonstrate that our strategy outperforms uncertainty-agnostic baselines on both synthetic and real-world datasets.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirical results demonstrate that our strategy outperforms uncertainty-agnostic baselines on both synthetic and real-world datasets. Code availability is flagged in the production record; the…

WHY NOW

Active Learning moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.

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Opportunity summary

Score7.0

PainAn active learning framework uses Linearized Optimal Transport and Gaussian Processes to select optimal measurement times for inferring measure-valued trajectories from sparse data.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

An active learning framework uses Linearized Optimal Transport and Gaussian Processes to select optimal measurement times for inferring measure-valued trajectories from sparse data.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

An active learning framework uses Linearized Optimal Transport and Gaussian Processes to select optimal measurement times for inferring measure-valued trajectories from sparse data.

Segment

Active Learning

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

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not classified

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Competitive landscape

An active learning framework uses Linearized Optimal Transport and Gaussian Processes to select optimal measurement times for inferring measure-valued trajectories from sparse data.

Segment

Active Learning

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Active Timepoint Selection for Learning Measure-Valued Trajectories

Active Timepoint Selection for Learning Measure-Valued Trajectories

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