Blog posts

2022

Scenarios of Confounding

less than 1 minute read

Published:

Causality and causal inference deal with expressing and reasoning about relationships of cause and effects, and structural causal model provide a rigorous formalism to assess causality.

2021

Category-Theoretical Evaluation of Abstraction between Causal Models

less than 1 minute read

Published:

Causal models offer a rigorous formalism to express causal relations between variables of interest. Causal systems may be represented at different levels of granularity or abstraction; think, for example, to microscopic and macroscopic descriptions of thermodynamics systems. Reasoning about the relationship between causal models at different levels of abstraction is a non trivial problem.

2020

Group Decision Making via Differentiable Programming

less than 1 minute read

Published:

Differentiable programming (also known as software 2.0) offers a novel approach to coding, focused on defining parametrized differentiable model to solve a problem instead of coding a precise algorithm. In this post we explore the use of this coding paradigm to solve the problem of consensus reaching in group-decision making.

2019

Bayesian Coresets and Edward

less than 1 minute read

Published:

Modern datasets often contain a large number of redundant samples, making the storing of data and the learning of models expensive. Coreset computation is an approach to reduce the amount of samples by selecting (and weighting) informative samples and discarding redundant ones.