16 citations · 46 across the 7 of their papers we have counts for
7 papers
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster +9
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery…
CoRGi: Content-Rich Graph Neural Networks with Attention
Jooyeon Kim, Angus Lamb, Simon Woodhead +3
Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges). However, such projections often miss important and rich informat…
Simultaneous Missing Value Imputation and Structure Learning with Groups
Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb +5
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve. One typical scenario is discovering th…
Contextual HyperNetworks for Novel Feature Adaptation
Angus Lamb, Evgeny Saveliev, Yingzhen Li +7
While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challeng…
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge
Zichao Wang, Angus Lamb, Evgeny Saveliev +9
This competition concerns educational diagnostic questions, which are pedagogically effective, multiple-choice questions (MCQs) whose distractors embody misconceptions. With a larg…
A Study on Efficiency in Continual Learning Inspired by Human Learning
Philip J. Ball, Yingzhen Li, Angus Lamb +1
Humans are efficient continual learning systems; we continually learn new skills from birth with finite cells and resources. Our learning is highly optimized both in terms of capac…