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11/23/2022
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors...
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09/24/2022
Unsupervised Model-based Pre-training for Data-efficient Control from Pixels
Controlling artificial agents from visual sensory data is an arduous tas...
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07/22/2021
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task to which ...
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07/21/2021
Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA
It can be argued that finding an interpretable low-dimensional represent...
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06/14/2021
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Learning the causal structure that underlies data is a crucial step towa...
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04/14/2021
Can Active Learning Preemptively Mitigate Fairness Issues?
Dataset bias is one of the prevailing causes of unfairness in machine le...
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03/30/2021
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Remote sensing and automatic earth monitoring are key to solve global-sc...
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03/18/2021
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable atten...
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11/14/2020
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Cattle farming is responsible for 8.8% of greenhouse gas emissions world...
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09/14/2020
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introdu...
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07/03/2020
Differentiable Causal Discovery from Interventional Data
Discovering causal relationships in data is a challenging task that invo...
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06/17/2020
Bayesian active learning for production, a systematic study and a reusable library
Active learning is able to reduce the amount of labelling effort by usin...
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03/09/2020
Embedding Propagation: Smoother Manifold for Few-Shot Classification
Few-shot classification is challenging because the data distribution of ...
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10/21/2019
Quantifying the Carbon Emissions of Machine Learning
From an environmental standpoint, there are a few crucial aspects of tra...
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06/10/2019
Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly ...
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06/10/2019
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we...
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05/28/2019
Adaptive Deep Kernel Learning
Deep kernel learning provides an elegant and principled framework for co...
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05/13/2019
Hierarchical Importance Weighted Autoencoders
Importance weighted variational inference (Burda et al., 2015) uses mult...
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09/06/2018
Improving Explorability in Variational Inference with Annealed Variational Objectives
Despite the advances in the representational capacity of approximate dis...
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06/20/2018
Uncertainty in Multitask Transfer Learning
Using variational Bayes neural networks, we develop an algorithm capable...
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05/23/2018
TADAM: Task dependent adaptive metric for improved few-shot learning
Few-shot learning has become essential for producing models that general...
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04/03/2018
Neural Autoregressive Flows
Normalizing flows and autoregressive models have been successfully combi...
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12/13/2017
Deep Prior
The recent literature on deep learning offers new tools to learn a rich ...
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10/13/2017
Bayesian Hypernetworks
We propose Bayesian hypernetworks: a framework for approximate Bayesian ...
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11/06/2016
Hierarchical Question Answering for Long Documents
We present a framework for question answering that can efficiently scale...
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08/11/2016
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
We present WikiReading, a large-scale natural language understanding tas...
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05/27/2016
PAC-Bayesian Theory Meets Bayesian Inference
We exhibit a strong link between frequentist PAC-Bayesian risk bounds an...
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02/04/2014