Search Results for author:
Found 32 papers, 12 papers with code
Optimal Transport for Structure Learning Under Missing Data
Merely filling in missing values with existing imputation methods and subsequently applying structure learning on the complete data is empirical shown to be sub-optimal.
Bayesian Factorised Granger-Causal Graphs For Multivariate Time-series Data
We study the problem of automatically discovering Granger causal relations from observational multivariate time-series data.
Variational DAG Estimation via State Augmentation With Stochastic Permutations
Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG), from observational data is a statistically and computationally hard problem with essential applications in areas such as causal discovery.
Contextual Directed Acyclic Graphs
Estimating the structure of directed acyclic graphs (DAGs) from observational data remains a significant challenge in machine learning.
Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimators
We propose the use of an alternative estimator based on the cross-entropy of the joint model distribution and a flexible proposal distribution.
Free-Form Variational Inference for Gaussian Process State-Space Models
However, inference in GPSSMs is computationally and statistically challenging due to the large number of latent variables in the model and the strong temporal dependencies between them.
Recurrent Neural Networks and Universal Approximation of Bayesian Filters
We consider the Bayesian optimal filtering problem: i. e. estimating some conditional statistics of a latent time-series signal from an observation sequence.
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
Learning useful node and graph representations with graph neural networks (GNNs) is a challenging task.
Optimizing Sequential Experimental Design with Deep Reinforcement Learning
Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging.
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs).
Model Selection for Bayesian Autoencoders
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization.
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Making predictions and quantifying their uncertainty when the input data is sequential is a fundamental learning challenge, recently attracting increasing attention.
BORE: Bayesian Optimization by Density-Ratio Estimation
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods.
Distribution Regression for Sequential Data
In this paper, we develop a rigorous mathematical framework for distribution regression where inputs are complex data streams.
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models.
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
We show that QP matches quantile functions rather than moments as in EP and has the same mean update but a smaller variance update than EP, thereby alleviating EP's tendency to over-estimate posterior variances.
Structured Variational Inference in Continuous Cox Process Models
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logistic sigmoid function.
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks.
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations
We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP) priors that allow nonzero covariance between grouped latent functions.
Grouped Gaussian Processes for Solar Power Prediction
We consider multi-task regression models where the observations are assumed to be a linear combination of several latent node functions and weight functions, which are both drawn from Gaussian process priors.
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from the other domain.
Calibrating Deep Convolutional Gaussian Processes
The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions.
Semi-parametric Network Structure Discovery Models
We propose a network structure discovery model for continuous observations that generalizes linear causal models by incorporating a Gaussian process (GP) prior on a network-independent component, and random sparsity and weight matrices as the network-dependent parameters.
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference; (ii) flexible kernels; and (iii) objective functions for hyperparameter learning alternative to the marginal likelihood.
Random Feature Expansions for Deep Gaussian Processes
The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty.
Gray-box inference for structured Gaussian process models
We develop an automated variational inference method for Bayesian structured prediction problems with Gaussian process (GP) priors and linear-chain likelihoods.
Generic Inference in Latent Gaussian Process Models
We evaluate our approach quantitatively and qualitatively with experiments on small datasets, medium-scale datasets and large datasets, showing its competitiveness under different likelihood models and sparsity levels.
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods
We propose a sparse method for scalable automated variational inference (AVI) in a large class of models with Gaussian process (GP) priors, multiple latent functions, multiple outputs and non-linear likelihoods.
Automated Variational Inference for Gaussian Process Models
Using a mixture of Gaussians as the variational distribution, we show that (i) the variational objective and its gradients can be approximated efficiently via sampling from univariate Gaussian distributions and (ii) the gradients of the GP hyperparameters can be obtained analytically regardless of the model likelihood.
Extended and Unscented Gaussian Processes
We present two new methods for inference in Gaussian process (GP) models with general nonlinear likelihoods.
Improving Topic Coherence with Regularized Topic Models
To overcome this, we propose two methods to regularize the learning of topic models.
Gaussian Process Preference Elicitation
Bayesian approaches to preference elicitation (PE) are particularly attractive due to their ability to explicitly model uncertainty in users' latent utility functions.
