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02/21/2023
Differentiable Multi-Target Causal Bayesian Experimental Design
We introduce a gradient-based approach for the problem of Bayesian optim...
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11/24/2022
Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery
Inferring causal structure from data is a challenging task of fundamenta...
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10/24/2022
Learning Latent Structural Causal Models
Causal learning has long concerned itself with the accurate recovery of ...
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07/12/2022
Latent Variable Models for Bayesian Causal Discovery
Learning predictors that do not rely on spurious correlations involves b...
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03/03/2022
Interventions, Where and How? Experimental Design for Causal Models at Scale
Causal discovery from observational and interventional data is challengi...
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09/06/2021
Learning Neural Causal Models with Active Interventions
Discovering causal structures from data is a challenging inference probl...
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06/14/2021
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Learning the causal structure that underlies data is a crucial step towa...
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06/14/2020
Structural Autoencoders Improve Representations for Generation and Transfer
We study the problem of structuring a learned representation to signific...
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07/23/2019
Noise Contrastive Variational Autoencoders
We take steps towards understanding the "posterior collapse (PC)" diffic...
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03/08/2018
Preserving Semantic Relations for Zero-Shot Learning
Zero-shot learning has gained popularity due to its potential to scale r...
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11/14/2016
Selfie Detection by Synergy-Constraint Based Convolutional Neural Network
Categorisation of huge amount of data on the multimedia platform is a cr...
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11/01/2016