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06/08/2023
Task-specific experimental design for treatment effect estimation
Understanding causality should be a core requirement of any attempt to b...
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10/23/2020
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy
In recent years, the collection and sharing of individuals' private data...
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10/14/2020
Explainability for fair machine learning
As the decisions made or influenced by machine learning models increasin...
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10/14/2020
Human-interpretable model explainability on high-dimensional data
The importance of explainability in machine learning continues to grow, ...
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10/07/2020
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders
Probabilistic models with hierarchical-latent-variable structures provid...
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10/07/2020
Learning disentangled representations with the Wasserstein Autoencoder
Disentangled representation learning has undoubtedly benefited from obje...
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06/01/2020
Shapley-based explainability on the data manifold
Explainability in machine learning is crucial for iterative model develo...
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10/14/2019
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
Explaining AI systems is fundamental both to the development of high per...
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02/18/2019
Parenting: Safe Reinforcement Learning from Human Input
Autonomous agents trained via reinforcement learning present numerous sa...
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02/08/2019
Invariant-equivariant representation learning for multi-class data
Representations learnt through deep neural networks tend to be highly in...
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06/12/2018
Improving latent variable descriptiveness with AutoGen
Powerful generative models, particularly in Natural Language Modelling, ...
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06/12/2018
Gaussian mixture models with Wasserstein distance
Generative models with both discrete and continuous latent variables are...
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04/25/2018
