
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task to which ...
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Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA
It can be argued that finding an interpretable lowdimensional represent...
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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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Can Active Learning Preemptively Mitigate Fairness Issues?
Dataset bias is one of the prevailing causes of unfairness in machine le...
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Seasonal Contrast: Unsupervised PreTraining from Uncurated Remote Sensing Data
Remote sensing and automatic earth monitoring are key to solve globalsc...
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Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable atten...
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Counting Cows: Tracking Illegal Cattle Ranching From HighResolution Satellite Imagery
Cattle farming is responsible for 8.8% of greenhouse gas emissions world...
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Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introdu...
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Differentiable Causal Discovery from Interventional Data
Discovering causal relationships in data is a challenging task that invo...
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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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Embedding Propagation: Smoother Manifold for FewShot Classification
Fewshot classification is challenging because the data distribution of ...
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Quantifying the Carbon Emissions of Machine Learning
From an environmental standpoint, there are a few crucial aspects of tra...
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Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly ...
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Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we...
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Adaptive Deep Kernel Learning
Deep kernel learning provides an elegant and principled framework for co...
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Hierarchical Importance Weighted Autoencoders
Importance weighted variational inference (Burda et al., 2015) uses mult...
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Improving Explorability in Variational Inference with Annealed Variational Objectives
Despite the advances in the representational capacity of approximate dis...
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Uncertainty in Multitask Transfer Learning
Using variational Bayes neural networks, we develop an algorithm capable...
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TADAM: Task dependent adaptive metric for improved fewshot learning
Fewshot learning has become essential for producing models that general...
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Neural Autoregressive Flows
Normalizing flows and autoregressive models have been successfully combi...
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Deep Prior
The recent literature on deep learning offers new tools to learn a rich ...
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Bayesian Hypernetworks
We propose Bayesian hypernetworks: a framework for approximate Bayesian ...
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Hierarchical Question Answering for Long Documents
We present a framework for question answering that can efficiently scale...
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WikiReading: A Novel Largescale Language Understanding Task over Wikipedia
We present WikiReading, a largescale natural language understanding tas...
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PACBayesian Theory Meets Bayesian Inference
We exhibit a strong link between frequentist PACBayesian risk bounds an...
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Sequential ModelBased Ensemble Optimization
One of the most tedious tasks in the application of machine learning is ...
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Alexandre Lacoste
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