# Hedging on the Frontier: Learning New Tasks with Few Samples

arXiv: https://arxiv.org/abs/2605.30997 · HTML: https://mlsanity.com/paper/2605.30997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 68 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 4 of 4)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Help Me Help You: The Aggregate Value of Source and Target Data in Transfer Learning](https://mlsanity.com/paper/2408.16189/) (2024)
- [Ratio Covers of Convex Sets and Optimal Mixture Density Estimation](https://mlsanity.com/paper/2602.16142/) (2026)

## Built on by (most cited first, 1 of 1)

- [Guided Uncertainty-Aware Robust Domain Transfer](https://mlsanity.com/paper/2609.35654/) (2026)

## Abstract (arXiv)

When a learner faces a new task with few samples, it must leverage any available side information. In practice, this often comes in the form of model evaluations on related tasks in public benchmarks. A key question then is how to model task relatedness such that it is both realistic and the benchmark evaluations lead to provable gains. Empirically, we observe that weak monotonicity is often approximately satisfied: if a model dominates another on many benchmarks, it also tends to outperform on the new task. We explore the statistical complexity of learning under (approximate) weak monotonicity, leveraging it within two learning paradigms: transfer learning and model selection aggregation. We show that not only can we prune the model class based on monotonicity, but we can also further adapt to the geometry of the available trade-offs by hedging on the frontier.