# Meta-Learning surrogate models for sequential decision making

arXiv: https://arxiv.org/abs/1903.11907 · HTML: https://mlsanity.com/paper/1903.11907/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling](https://mlsanity.com/paper/2207.04179/) (2022)
- [Latent Bottlenecked Attentive Neural Processes](https://mlsanity.com/paper/2211.08458/) (2022)
- [Meta-Learning Sparse Compression Networks](https://mlsanity.com/paper/2205.08957/) (2022)
- [SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials](https://mlsanity.com/paper/2404.04963/) (2024)
- [NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports](https://mlsanity.com/paper/2305.03598/) (2023)
- [Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models](https://mlsanity.com/paper/2102.08291/) (2021)
- [Group Equivariant Conditional Neural Processes](https://mlsanity.com/paper/2102.08759/) (2021)
- [Neural Processes with Stochastic Attention: Paying more attention to the context dataset](https://mlsanity.com/paper/2204.05449/) (2022)
- [Learning Large-scale Neural Fields via Context Pruned Meta-Learning](https://mlsanity.com/paper/2302.00617/) (2023)
- [Bridge the Inference Gaps of Neural Processes via Expectation Maximization](https://mlsanity.com/paper/2501.03264/) (2025)
- [SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data](https://mlsanity.com/paper/2305.02993/) (2023)
- [SplAgger: Split Aggregation for Meta-Reinforcement Learning](https://mlsanity.com/paper/2403.03020/) (2024)
- [On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR](https://mlsanity.com/paper/2312.05465/) (2023)
- [Differentiable User Models](https://mlsanity.com/paper/2211.16277/) (2022)

## Abstract (arXiv)

We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning. This is accomplished by a probabilistic model-based approach that explains observed data while capturing predictive uncertainty during the decision making process. Crucially, this probabilistic model is chosen to be a Meta-Learning system that allows learning from a distribution of related problems, allowing data efficient adaptation to a target task. As a suitable instantiation of this framework, we explore the use of Neural processes due to statistical and computational desiderata. We apply our framework to a broad range of problem domains, such as control problems, recommender systems and adversarial attacks on RL agents, demonstrating an efficient and general black-box learning approach.