431 citations · 799 across the 18 of their papers we have counts for
20 papers
Scalable Meta-Learning via Mixed-Mode Differentiation
Iurii Kemaev, Dan A Calian, Luisa M Zintgraf +2
Gradient-based bilevel optimisation is a powerful technique with applications in hyperparameter optimisation, task adaptation, algorithm discovery, meta-learning more broadly, and…
DataRater: Meta-Learned Dataset Curation
Dan A. Calian, Gregory Farquhar, Iurii Kemaev +9
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…
A Tutorial on Meta-Reinforcement Learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu +4
While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficien…
Generalized Beliefs for Cooperative AI
Darius Muglich, Luisa Zintgraf, Christian Schroeder de Witt +2
Self-play is a common paradigm for constructing solutions in Markov games that can yield optimal policies in collaborative settings. However, these policies often adopt highly-spec…
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf +4
Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for t…
On the Practical Consistency of Meta-Reinforcement Learning Algorithms
Zheng Xiong, Luisa Zintgraf, Jacob Beck +2
Consistency is the theoretical property of a meta learning algorithm that ensures that, under certain assumptions, it can adapt to any task at test time. An open question is whethe…
Communicating via Markov Decision Processes
Samuel Sokota, Christian Schroeder de Witt, Maximilian Igl +6
We consider the problem of communicating exogenous information by means of Markov decision process trajectories. This setting, which we call a Markov coding game (MCG), generalizes…
Optimizing piano practice with a utility-based scaffold
Alexandra Moringen, Sören Rüttgers, Luisa Zintgraf +2
A typical part of learning to play the piano is the progression through a series of practice units that focus on individual dimensions of the skill, such as hand coordination, corr…
A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings
Eltayeb Ahmed, Luisa Zintgraf, Christian A. Schroeder de Witt +1
In this work we explore an auxiliary loss useful for reinforcement learning in environments where strong performing agents are required to be able to navigate a spatial environment…
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
Daniela Massiceti, Luisa Zintgraf, John Bronskill +6
Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new…
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi +15
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcemen…
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
Luisa Zintgraf, Sam Devlin, Kamil Ciosek +2
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 lea…
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
Luisa Zintgraf, Leo Feng, Cong Lu +4
To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep. One way to learn such behaviour…
VIABLE: Fast Adaptation via Backpropagating Learned Loss
Leo Feng, Luisa Zintgraf, Bei Peng +1
In few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in minimising, such as the mean-squared-error loss for a re…
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl +4
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions…
Fast Context Adaptation via Meta-Learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin +2
We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model par…
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le +2
Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinfo…
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
Luisa M Zintgraf, Diederik M Roijers, Sjoerd Linders +2
In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profi…
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel +1
This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highl…
A New Method to Visualize Deep Neural Networks
Luisa M. Zintgraf, Taco S. Cohen, Max Welling
We present a method for visualising the response of a deep neural network to a specific input. For image data for instance our method will highlight areas that provide evidence in…