Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Transformed distribution matching for missing value imputation
We study the problem of imputing missing values in a dataset, which has important
applications in many domains. The key to missing value imputation is to capture the data …
applications in many domains. The key to missing value imputation is to capture the data …
Cuts: Neural causal discovery from irregular time-series data
Causal discovery from time-series data has been a central task in machine learning.
Recently, Granger causality inference is gaining momentum due to its good explainability …
Recently, Granger causality inference is gaining momentum due to its good explainability …
CUTS+: High-dimensional causal discovery from irregular time-series
Causal discovery in time-series is a fundamental problem in the machine learning
community, enabling causal reasoning and decision-making in complex scenarios …
community, enabling causal reasoning and decision-making in complex scenarios …
Causal reasoning in the presence of latent confounders via neural ADMG learning
Latent confounding has been a long-standing obstacle for causal reasoning from
observational data. One popular approach is to model the data using acyclic directed mixed …
observational data. One popular approach is to model the data using acyclic directed mixed …
Optimal transport for structure learning under missing data
Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma.
While the goal is to recover the true causal structure, robust imputation requires considering …
While the goal is to recover the true causal structure, robust imputation requires considering …
Generative adversarial networks for imputing sparse learning performance
Learning performance data, such as correct or incorrect responses to questions in Intelligent
Tutoring Systems (ITSs) is crucial for tracking and assessing the learners' progress and …
Tutoring Systems (ITSs) is crucial for tracking and assessing the learners' progress and …
Causal inference via predictive coding
Bayesian and causal inference are fundamental processes for intelligence. Bayesian
inference models observations: what can be inferred about y if we observe a related variable …
inference models observations: what can be inferred about y if we observe a related variable …
RedCore: Relative Advantage Aware Cross-Modal Representation Learning for Missing Modalities with Imbalanced Missing Rates
Multimodal learning is susceptible to modality missing, which poses a major obstacle for its
practical applications and, thus, invigorates increasing research interest. In this paper, we …
practical applications and, thus, invigorates increasing research interest. In this paper, we …