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Edward Hu

Alumni

Publications

GFlowNets and Variational Inference
Nikolay Malkin
Salem Lahlou
Tristan Deleu
Xu Ji
Edward Hu
Katie Everett
Dinghuai Zhang
Yoshua Bengio
This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically us… (voir plus)ed to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demonstrate that, in certain cases, VI algorithms are equivalent to special cases of GFlowNets in the sense of equality of expected gradients of their learning objectives. We then point out the differences between the two families and show how these differences emerge experimentally. Notably, GFlowNets, which borrow ideas from reinforcement learning, are more amenable than VI to off-policy training without the cost of high gradient variance induced by importance sampling. We argue that this property of GFlowNets can provide advantages for capturing diversity in multimodal target distributions.
2023-04-30
International Conference on Learning Representations (Accept: poster)
doi.org
openreview.net