OxCSML : People : Charline Le Lan

Probabilistic Inference, Deep Learning, Reinforcement Learning

I am a PhD student supervised by Prof Yee Whye Teh at the University of Oxford. My current research interests are in deep generative models, representation learning and reinforcement learning.

Publications

2019

E. Mathieu , C. Le Lan , C. J. Maddison , R. Tomioka , Y. W. Teh , Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders, in Advances in Neural Information Processing Systems 32, 2019, 12565–12576.
The Variational Auto-Encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, traditional VAEs map data in a Euclidean latent space which cannot efficiently embed tree-like structures. Hyperbolic spaces with negative curvature can. We therefore endow VAEs with a Poincaré ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space. We empirically show better generalisation to unseen data than the Euclidean counterpart, and can qualitatively and quantitatively better recover hierarchical structures.
```
@inproceedings{mathieu2019Continuous,
  title = {Continuous Hierarchical Representations with Poincar\'{e} Variational Auto-Encoders},
  author = {Mathieu, Emile and Le Lan, Charline and Maddison, Chris J. and Tomioka, Ryota and Teh, Yee Whye},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages = {12565--12576},
  year = {2019},
  publisher = {Curran Associates, Inc.}
}
```