Multi-modal data-sets are ubiquitous in modern applications, and multi-m...
Generative Models (GMs) have attracted considerable attention due to the...
We introduce functional diffusion processes (FDPs), which generalize
tra...
While the promises of Multi-Task Learning (MTL) are attractive,
characte...
Score-based diffusion models are a class of generative models whose dyna...
When learning to act in a stochastic, partially observable environment, ...
When learning to behave in a stochastic environment where safety is crit...
Anomaly detection in time series is a complex task that has been widely
...
In Multi-Task Learning (MTL), it is a common practice to train multi-tas...
We revisit the theoretical properties of Hamiltonian stochastic differen...
We develop a novel method for carrying out model selection for Bayesian
...
Approximations to Gaussian processes based on inducing variables, combin...
A large part of the literature on learning disentangled representations
...
Multi-task learning has gained popularity due to the advantages it provi...
In this work we define a unified mathematical framework to deepen our
un...
In this paper, we employ variational arguments to establish a connection...
Accurate travel products price forecasting is a highly desired feature t...
We present a novel method - LIBRE - to learn an interpretable classifier...
Large scale machine learning is increasingly relying on distributed
opti...
The identification of anomalies in temporal data is a core component of
...
Stochastic variational inference is an established way to carry out
appr...
Nowadays, data-centers are largely under-utilized because resource alloc...
In this paper, we study the problem of deriving fast and accurate
classi...
The wide adoption of Convolutional Neural Networks (CNNs) in application...
In modern large-scale distributed systems, analytics jobs submitted by
v...
We consider elastic resource provisioning in the cloud, focusing on in-m...
The composition of multiple Gaussian Processes as a Deep Gaussian Proces...