Posts by Tags

Bayes

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.

PSCM

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.

SCM

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.

abstraction

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.

abstraction error

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.

category theory

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.

causal models

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.

causality

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.

confounders

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.

consensus

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.

coreset

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.

counterfactuals

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.

covid19

differentiable programming

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.

do-calculus

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.

dowhy

edward

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.

epidemiology

feature distribution learning

group decision making

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.

interventions

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.

numpy

openAI gym

phising

pymc3

qiskit

quantum

reinforcement learning

sparse filtering

tensorflow

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.

transformation

unsupervised learning