58 citations · 249 across the 21 of their papers we have counts for
15 papers · 1 filter
Local Constraint-Based Causal Discovery under Selection Bias
Philip Versteeg, Cheng Zhang, Joris M. Mooij
We consider the problem of discovering causal relations from independence constraints selection bias in addition to confounding is present. While the seminal FCI algorithm is sound…
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…
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Amit Sharma, Vasilis Syrgkanis, Cheng Zhang +1
Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and…
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha +3
Making evidence based decisions requires data. However for real-world applications, the privacy of data is critical. Using synthetic data which reflects certain statistical propert…
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…
Reinforcement Learning with Efficient Active Feature Acquisition
Haiyan Yin, Yingzhen Li, Sinno Jialin Pan +2
Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem. To be successful, an agent needs to efficiently gath…
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…
DRIFT: Deep Reinforcement Learning for Functional Software Testing
Luke Harries, Rebekah Storan Clarke, Timothy Chapman +10
Efficient software testing is essential for productive software development and reliable user experiences. As human testing is inefficient and expensive, automated software testing…
Meta-Learning Divergences of Variational Inference
Ruqi Zhang, Yingzhen Li, Christopher De Sa +2
Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and broad applicability. Crucial to the performance of VI i…
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato +2
Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets. Heterogeneity arises from data containing different types of f…
A Causal View on Robustness of Neural Networks
Cheng Zhang, Kun Zhang, Yingzhen Li
We present a causal view on the robustness of neural networks against input manipulations, which applies not only to traditional classification tasks but also to general measuremen…
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li +4
The ability for policies to generalize to new environments is key to the broad application of RL agents. A promising approach to prevent an agent's policy from overfitting to a lim…
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
Wenbo Gong, Sebastian Tschiatschek, Richard Turner +3
In this paper we introduce the ice-start problem, i.e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquirin…
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Hiske Overweg, Anna-Lena Popkes, Ari Ercole +4
Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneous data generated as part of routine clinical care. In recent yea…
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla +3
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may deci…