81 citations · 124 across the 7 of their papers we have counts for
7 papers
Fast Adaptation with Linearized Neural Networks
Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno +2
The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural n…
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams
Charlie Dickens, Eric Meissner, Pablo G. Moreno +1
Anomaly detection at scale is an extremely challenging problem of great practicality. When data is large and high-dimensional, it can be difficult to detect which observations do n…
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Francesco Tonolini, Pablo G. Moreno, Andreas Damianou +1
We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean valu…
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Shell Xu Hu, Pablo G. Moreno, Yang Xiao +4
We propose a meta-learning approach that learns from multiple tasks in a transductive setting, by leveraging the unlabeled query set in addition to the support set to generate a mo…
Preferential Batch Bayesian Optimization
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen +3
Most research in Bayesian optimization (BO) has focused on \emph{direct feedback} scenarios, where one has access to exact values of some expensive-to-evaluate objective. This dire…
Transferring Knowledge across Learning Processes
Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence +1
In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a sou…
Bayesian Nonparametric Crowdsourcing
Pablo G. Moreno, Yee Whye Teh, Fernando Perez-Cruz +1
Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce r…