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07/18/2023
Self-Compatibility: Evaluating Causal Discovery without Ground Truth
As causal ground truth is incredibly rare, causal discovery algorithms a...
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05/11/2023
Reinterpreting causal discovery as the task of predicting unobserved joint statistics
If X,Y,Z denote sets of random variables, two different data sources may...
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11/03/2022
A Consistent Estimator for Confounding Strength
Regression on observational data can fail to capture a causal relationsh...
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02/18/2022
Interpolation and Regularization for Causal Learning
We study the problem of learning causal models from observational data t...
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12/07/2021
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
In recent years, several results in the supervised learning setting sugg...
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11/18/2021
Causal Forecasting:Generalization Bounds for Autoregressive Models
Despite the increasing relevance of forecasting methods, the causal impl...
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10/18/2021
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Despite the ubiquity of kernel-based clustering, surprisingly few statis...
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10/06/2021
Graphon based Clustering and Testing of Networks: Algorithms and Theory
Network-valued data are encountered in a wide range of applications and ...
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12/03/2019
Large scale representation learning from triplet comparisons
In this paper, we discuss the fundamental problem of representation lear...
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12/01/2019