1 code implementation • 4 Feb 2022 • Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
1 code implementation • 15 Oct 2021 • Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
no code implementations • 10 Oct 2021 • Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zheng, Miltiadis Allamanis
Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges).
no code implementations • 29 Sep 2021 • Tomas Geffner, Emre Kiciman, Angus Lamb, Martin Kukla, Miltiadis Allamanis, Cheng Zhang
Current causal discovery methods either fail to scale, model only limited forms of functional relationships, or cannot handle missing values.
no code implementations • 12 Apr 2021 • Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang
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 challenge, as neural networks are commonly trained to produce a fixed output dimension.
no code implementations • 28 Oct 2020 • Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang
We study a setting where the pruning phase is given a time budget, and identify connections between iterative pruning and multiple sleep cycles in humans.
no code implementations • 23 Jul 2020 • Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers.