| Time | Title | Speaker | |
|---|---|---|---|
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9:15 – 9:45
|
Registration & Welcome tea/coffee
|
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|
9:45 – 10:05
|
Learning to Code: Machine Learning for Program Induction
|
Alex Gaunt
|
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|
10:05 – 10:25
|
Learning Program Representations: Symbols to Vectors to Semantics
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|
10:25 – 10:45
|
TBD
|
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|
10:45 – 11:05
|
From GANs to Variational Divergence Minimization
|
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|
11:05 – 11:30
|
Coffee break
|
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|
11:30 – 11:50
|
Automatic Discovery of the Statistical Types of Variables in a Dataset
|
Isabel Valera
|
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|
11:50 – 12:10
|
Approximate Inference with Amortised MCMC
|
Yingzhen Li
|
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|
12:10 – 12:30
|
The Automatic Statistician: a project update
|
Zoubin Ghahramani
|
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|
12:30 – 12:50
|
The Supervised Word Mover’s Distance
|
Matt Kusner
|
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|
1:00 – 2:00
|
Lunch
|
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|
2:00 – 2:20
|
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
|
Jose Miguel Hernandez Lobato
|
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|
2:20 – 2:40
|
Bayesian optimisation in many dimensions with bespoke models
|
Adrian Weller
|
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|
2:40 – 3:00
|
Grammar Variational Autoencoder
|
Brooks Paige
|
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|
3:00 – 3:20
|
Invertible Transformations for Bayesian Neural Network Inference
|
Amar Shah
|
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|
3:20 – 4:00
|
Coffee break
|
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|
4:00 – 4:20
|
AI for Healthcare
|
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|
4:20 – 4:40
|
Project Alexandria: a web scale probabilistic program for unsupervised knowledge base construction
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|
4:40 – 5:00
|
The Malmo Collaborative AI Challenge
|