← back

Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning

Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks do not coincide with the scores commonly used to quantify e

https://arxiv.org/abs/2607.14817v1 ↗
Thesis fit
Good fit

Within your typical scope; diligence still required.

Edit thesis
In your usual scope
Idea match None

How close the company’s idea is to your thesis statement

Sector ai

Overlap with sectors you care about

Geography Unknown

Location unknown — scores 0

Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”

Founder ↑ improving Traction → stable Idea vs market ↓ declining
Pass
Generate memo
Suggested next step: deprioritize

First-pass screen flags a clear non-starter (spam, no product ambition, agency/hobby). Skip the memo unless thesis fit is unusually high or a partner asks. Use Clear triage to remove the Advance / Needs review / Pass badge from Opportunities.

  • Looks like an academic paper, not a venture-backed product or company
  • No clear product, customer, or commercialization path described
  • Title/website point to arXiv research rather than a startup opportunity
Add / edit details

Correct facts used on the next screening or memo.

Similar baseline plays (YC · idea space)

Confident AI · Active
The LLM Eval and Observability Platform for AI Quality
founders not scraped yet
The Synthesis Company · Active
100x faster scientific evidence synthesis
founders not scraped yet
Outrove · Active
Synthetic populations for decisions
founders not scraped yet
Acely · Active
Every student's AI coach for college readiness
founders not scraped yet
Midship · Acquired
AI for SOX testing
founders not scraped yet