Publications (227)
Probabilistic Binary Neural Networks
Jorn W. T. Peters, Max Welling
Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a pr…
Hamiltonian ABC
Edward Meeds, Robert Leenders, Max Welling
Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood…
Private Topic Modeling
Mijung Park, James Foulds, Kamalika Chaudhuri +1
We develop a privatised stochastic variational inference method for Latent Dirichlet Allocation (LDA). The iterative nature of stochastic variational inference presents challenges:…
DP-EM: Differentially Private Expectation Maximization
Mijung Park, Jimmy Foulds, Kamalika Chaudhuri +1
The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed.…
Contrastive Learning of Structured World Models
Thomas Kipf, Elise van der Pol, Max Welling
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from ra…
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation
James Foulds, Levi Boyles, Christopher Dubois +2
In the internet era there has been an explosion in the amount of digital text information available, leading to difficulties of scale for traditional inference algorithms for topic…