15.1k citations · 36.9k across the 152 of their papers we have counts for
29 papers · 1 filter
Combining Interventional and Observational Data Using Causal Reductions
Maximilian Ilse, Patrick Forré, Max Welling +1
Unobserved confounding is one of the main challenges when estimating causal effects. We propose a causal reduction method that, given a causal model, replaces an arbitrary number o…
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel
Changyong Oh, Roberto Bondesan, Efstratios Gavves +1
In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive-to-evaluate objectives. We first introdu…
Mixed Variable Bayesian Optimization with Frequency Modulated Kernels
Changyong Oh, Efstratios Gavves, Max Welling
The sample efficiency of Bayesian optimization(BO) is often boosted by Gaussian Process(GP) surrogate models. However, on mixed variable spaces, surrogate models other than GPs are…
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini +2
Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural images. This paper introduces two extensions of flows and diffusion for c…
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…
Combining Generative and Discriminative Models for Hybrid Inference
Victor Garcia Satorras, Zeynep Akata, Max Welling
A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical mo…
DIVA: Domain Invariant Variational Autoencoders
Maximilian Ilse, Jakub M. Tomczak, Christos Louizos +1
We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We…
Combinatorial Bayesian Optimization using the Graph Cartesian Product
Changyong Oh, Jakub M. Tomczak, Efstratios Gavves +1
This paper focuses on Bayesian Optimization (BO) for objectives on combinatorial search spaces, including ordinal and categorical variables. Despite the abundance of potential appl…
The Deep Weight Prior
Andrei Atanov, Arsenii Ashukha, Kirill Struminsky +2
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior dist…
BOCK : Bayesian Optimization with Cylindrical Kernels
ChangYong Oh, Efstratios Gavves, Max Welling
A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper…
Sylvester Normalizing Flows for Variational Inference
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak +1
Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce…
Neural Relational Inference for Interacting Systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang +2
Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which ca…
Improved Bayesian Compression
Marco Federici, Karen Ullrich, Max Welling
Compression of Neural Networks (NN) has become a highly studied topic in recent years. The main reason for this is the demand for industrial scale usage of NNs such as deploying th…
Learning Sparse Neural Networks through Regularization
Christos Louizos, Max Welling, Diederik P. Kingma
We propose a practical method for norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularizat…
Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow
Jakub M. Tomczak, Max Welling
In this paper, we propose a new volume-preserving flow and show that it performs similarly to the linear general normalizing flow. The idea is to enrich a linear Inverse Autoregres…
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max Welling
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite…
Bayesian Compression for Deep Learning
Christos Louizos, Karen Ullrich, Max Welling
Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack t…
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem +3
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance,…
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos, Max Welling
We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment the approximate posterior in a variational setting for Bayesian neural networks. W…
Soft Weight-Sharing for Neural Network Compression
Karen Ullrich, Edward Meeds, Max Welling
The success of deep learning in numerous application domains created the de- sire to run and train them on mobile devices. This however, conflicts with their computationally, memor…
Variational Graph Auto-Encoders
Thomas N. Kipf, Max Welling
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes…
Variational Bayes In Private Settings (VIPS)
Mijung Park, James Foulds, Kamalika Chaudhuri +1
Many applications of Bayesian data analysis involve sensitive information, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving fram…
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:…
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Christos Louizos, Max Welling
We introduce a variational Bayesian neural network where the parameters are governed via a probability distribution on random matrices. Specifically, we employ a matrix variate Gau…