72 citations · 174 across the 14 of their papers we have counts for
19 papers · 1 filter
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation
Diego Martinez-Taboada, Dino Sejdinovic
The counterfactual distribution models the effect of the treatment in the untreated group. While most of the work focuses on the expected values of the treatment effect, one may be…
Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings
Diego Martinez-Taboada, Dino Sejdinovic
The problem of sequentially maximizing the expectation of a function seeks to maximize the expected value of a function of interest without having direct control on its features. I…
BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau, Jean-François Ton, Javier González +2
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we stud…
Benign Overfitting and Noisy Features
Zhu Li, Weijie Su, Dino Sejdinovic
Modern machine learning often operates in the regime where the number of parameters is much higher than the number of data points, with zero training loss and yet good generalizati…
Meta Learning for Causal Direction
Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu
The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus…
Large Scale Tensor Regression using Kernels and Variational Inference
Robert Hu, Geoff K. Nicholls, Dino Sejdinovic
We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakn…