Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

no code implementations • 4 Nov 2022 • Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, Laurent Charlin

We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model.

Learning Latent Structural Causal Models

no code implementations • 24 Oct 2022 • Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions.

Bayesian Inference Image Generation +1

Latent Variable Models for Bayesian Causal Discovery

no code implementations • 12 Jul 2022 • Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

Learning predictors that do not rely on spurious correlations involves building causal representations.

Bayesian Inference Causal Discovery

Using Deep Reinforcement Learning to Train and Evaluate Instructional Sequencing Policies for an Intelligent Tutoring System

no code implementations • 1 Jan 2021 • Jithendaraa Subramanian, David Mostow

STEP leverages the student model by representing the student’s knowledge state as a probability vector of knowing each skill and using the student’s estimated learning gains as its reward function to evaluate candidate policies.

Knowledge Tracing