On Constraint Definability in Tractable Probabilistic Models

01/29/2020
by   Ioannis Papantonis, et al.
10

Incorporating constraints is a major concern in probabilistic machine learning. A wide variety of problems require predictions to be integrated with reasoning about constraints, from modelling routes on maps to approving loan predictions. In the former, we may require the prediction model to respect the presence of physical paths between the nodes on the map, and in the latter, we may require that the prediction model respect fairness constraints that ensure that outcomes are not subject to bias. Broadly speaking, constraints may be probabilistic, logical or causal, but the overarching challenge is to determine if and how a model can be learnt that handles all the declared constraints. To the best of our knowledge, this is largely an open problem. In this paper, we consider a mathematical inquiry on how the learning of tractable probabilistic models, such as sum-product networks, is possible while incorporating constraints.

READ FULL TEXT
research
02/03/2022

Incorporating Sum Constraints into Multitask Gaussian Processes

Machine learning models can be improved by adapting them to respect exis...
research
02/20/2021

Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models

While probabilistic models are an important tool for studying causality,...
research
10/08/2018

Deep Tractable Probabilistic Models for Moral Responsibility

Moral responsibility is a major concern in automated decision-making, wi...
research
10/05/2019

On Tractable Computation of Expected Predictions

Computing expected predictions has many interesting applications in area...
research
04/01/2020

OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints

We present a new approach to the type inference problem for dynamic lang...
research
05/16/2019

Fairness in Machine Learning with Tractable Models

Machine Learning techniques have become pervasive across a range of diff...
research
05/25/2021

Prediction error quantification through probabilistic scaling – EXTENDED VERSION

In this paper, we address the probabilistic error quantification of a ge...

Please sign up or login with your details

Forgot password? Click here to reset