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Luisa Zintgraf

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NeurIPS Conference 2025 Conference Paper

DataRater: Meta-Learned Dataset Curation

Dan Andrei Calian
Greg Farquhar
Iurii Kemaev
Luisa Zintgraf
Matteo Hessel
Jeremy Shar
Junhyuk Oh
András György

The quality of foundation models depends heavily on their training data. Consequently, great efforts have been put into dataset curation. Yet most approaches rely on manual tuning of coarse-grained mixtures of large buckets of data, or filtering by hand-crafted heuristics. An approach that is ultimately more scalable (let alone more satisfying) is to \emph{learn} which data is actually valuable for training. This type of meta-learning could allow more sophisticated, fine-grained, and effective curation. Our proposed \emph{DataRater} is an instance of this idea. It estimates the value of training on any particular data point. This is done by meta-learning using `meta-gradients', with the objective of improving training efficiency on held out data. In extensive experiments across a range of model scales and datasets, we find that using our DataRater to filter data is highly effective, resulting in significantly improved compute efficiency.

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AAMAS Conference 2021 Conference Paper

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning

Luisa Zintgraf
Sam Devlin
Kamil Ciosek
Shimon Whiteson
Katja Hofmann

Agents that interact with other agents often do not know a priori what the other agents’ strategies are, but have to maximise their own online return while interacting with and learning about others. The optimal adaptive behaviour under uncertainty over the other agents’ strategies w. r. t. some prior can in principle be computed using the Interactive Bayesian Reinforcement Learning framework. Unfortunately, doing so is intractable in most settings, and existing approximation methods are restricted to small tasks. To overcome this, we propose to meta-learn (alongside the policy) approximate belief inference by combining sequential and hierarchical VAEs. We show empirically that our approach can learn a factorised belief model that separates the other agent’s permanent and temporal structure, and outperforms methods that sample from the approximate posterior or do not have this hierarchical structure. A full version of this work can be found in Zintgraf et al. [30].

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JMLR Journal 2021 Journal Article

VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning

Luisa Zintgraf
Sebastian Schulze
Cong Lu
Leo Feng
Maximilian Igl
Kyriacos Shiarlis
Yarin Gal
Katja Hofmann

Trading off exploration and exploitation in an unknown environment is key to maximising expected online return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but also on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is however intractable for all but the smallest tasks. In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn approximately Bayes-optimal policies for complex tasks. VariBAD simultaneously meta-learns a variational auto-encoder to perform approximate inference, and a policy that incorporates task uncertainty directly during action selection by conditioning on both the environment state and the approximate belief. In two toy domains, we illustrate how variBAD performs structured online exploration as a function of task uncertainty. We further evaluate variBAD on MuJoCo tasks widely used in meta-RL and show that it achieves higher online return than existing methods. On the recently proposed Meta-World ML1 benchmark, variBAD achieves state of the art results by a large margin, fully solving two out of the three ML1 tasks for the first time. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

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