activity
20182021
most citedEmpirical Bayes Transductive Meta-Learning with Synthetic Gradients

81 citations · 81 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ML2021

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…

cs.LG2020

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…

cs.LG2020

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…

cs.LG202081 cited

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…

cs.LG2018

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…