83 citations · 343 across the 46 of their papers we have counts for
6 papers · 1 filter
Good Initializations of Variational Bayes for Deep Models
Simone Rossi, Pietro Michiardi, Maurizio Filippone
Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models. While there have been effective proposals for good initializatio…
A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute Clusters
Francesco Pace, Dimitrios Milios, Damiano Carra +2
Nowadays, data-centers are largely under-utilized because resource allocation is based on reservation mechanisms which ignore actual resource utilization. Indeed, it is common to r…
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi +2
In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled a…
Calibrating Deep Convolutional Gaussian Processes
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham +2
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 abili…
Cache-based Multi-query Optimization for Data-intensive Scalable Computing Frameworks
Pietro Michiardi, Damiano Carra, Sara Migliorini
In modern large-scale distributed systems, analytics jobs submitted by various users often share similar work, for example scanning and processing the same subset of data. Instead…
Elastic Provisioning of Cloud Caches: a Cost-aware TTL Approach
Damiano Carra, Giovanni Neglia, Pietro Michiardi
We consider elastic resource provisioning in the cloud, focusing on in-memory key-value stores used as caches. Our goal is to dynamically scale resources to the traffic pattern min…