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Typing assumptions improve identification in causal discovery
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an …
Workshop at the International Conference on Machine Learning (ICML),  2021.
Article Citation Code
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing …
Workshop at the International Conference on Machine Learning (ICML),  2021.
Article Citation Code
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Cattle farming is responsible for 8.8% of greenhouse gas emissions worldwide. In addition to the methane emitted due to their digestive …
Workshop at the Neural Information Processing Systems (NeurIPS),  2020.
Article Citation Code
Like A Researcher Stating Broader Impact for the Very First Time
Like A Researcher Stating Broader Impact for the Very First Time
In requiring that a statement of broader impact accompany all submissions for this year’s conference, the NeurIPS program chairs …
Workshop at the Neural Information Processing Systems (NeurIPS),  2020.
Article Citation
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?
Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et …
Workshop at the Neural Information Processing Systems (NeurIPS),  2020.
Article Citation
Bayesian active learning for production, a systematic study and a reusable library
Bayesian active learning for production, a systematic study and a reusable library
Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific …
Workshop at the International Conference on Machine Learning (ICML),  2020.
Article Citation Code
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two …
Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR),  2020.
Article Citation Code
Gradient-Based Neural DAG Learning with Interventions
Gradient-Based Neural DAG Learning with Interventions
Decision making based on statistical association alone can be a dangerous en- deavor due to non-causal associations. Ideally, one would …
Workshop at the International Conference on Learning Representations (ICLR),  2020.
Article Citation Code Vidéo
Knowledge Hypergraphs: Prediction Beyond Binary Relations
Knowledge Hypergraphs: Prediction Beyond Binary Relations
Knowledge graphs store facts using relations between two entities. In this work, we address the question of link prediction in …
Workshop at the Association for the Advancement of Artificial Intelligence (AAAI),  2020.
Article Citation Code
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
We propose a Class-Based Styling method (CBS) that can map different styles for different object classes in real-time. CBS achieves …
Women in Machine Learning (WiML),  2019.
Article Citation Code