mlsanityData to 14 Oct 2026

Prospective Learning in Retrospect

arXiv 2507.07965 ↗First posted 10 Jul 2025cs.LG stat.MLcited by 1

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Abstract (arXiv)

In most real-world applications of artificial intelligence, the distributions of the data and the goals of the learners tend to change over time. The Probably Approximately Correct (PAC) learning framework, which underpins most machine learning algorithms, fails to account for dynamic data distributions and evolving objectives, often resulting in suboptimal performance. Prospective learning is a recently introduced mathematical framework that overcomes some of these limitations. We build on this framework to present preliminary results that improve the algorithm and numerical results, and extend prospective learning to sequential decision-making scenarios, specifically foraging. Code is available at: https://github.com/neurodata/prolearn2.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 10 Jul 2025.

Builds on

14 of 33 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  4. 2016OpenAI Gymcited by 1,736
  5. 2014Consistency of random forestscited by 85
  6. 2021Model Zoo: A Growing "Brain" That Learns Continuallycited by 54
  7. 2023Continual Learning as Computationally Constrained Reinforcement Learningcited by 37
  8. 2020Task Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updatescited by 34
  9. 2021Large Scale Prediction with Decision Treescited by 28
  10. 2022You Only Live Once: Single-Life Reinforcement Learningcited by 24
  11. 2020Defining Benchmarks for Continual Few-Shot Learningcited by 21
  12. 2022The Value of Out-of-Distribution Datacited by 12
2 more
  1. 2022Prospective Learning: Principled Extrapolation to the Futurecited by 6
  2. 2024Prospective Learning: Learning for a Dynamic Futurecited by 4

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Optimal control of the future via prospective learning with controlcited by 0