71 citations · 604 across the 67 of their papers we have counts for
4 papers · 2 filters
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Tom Rainforth
We introduce Support Decomposition Variational Inference (SDVI), a new variational inference (VI) approach for probabilistic programs with stochastic support. Existing approaches t…
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Tom Rainforth
The posterior in probabilistic programs with stochastic support decomposes as a weighted sum of the local posterior distributions associated with each possible program path. We sho…
Prediction-Oriented Bayesian Active Learning
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar +3
Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BA…
Incorporating Unlabelled Data into Bayesian Neural Networks
Mrinank Sharma, Tom Rainforth, Yee Whye Teh +1
Conventional Bayesian Neural Networks (BNNs) are unable to leverage unlabelled data to improve their predictions. To overcome this limitation, we introduce Self-Supervised Bayesian…