Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Choi, YooJung"'
Autor:
Ciotinga, Adrian, Choi, YooJung
We introduce a novel optimal transport framework for probabilistic circuits (PCs). While it has been shown recently that divergences between distributions represented as certain classes of PCs can be computed tractably, to the best of our knowledge,
Externí odkaz:
http://arxiv.org/abs/2410.13061
Autor:
Amarilli, Antoine, Arenas, Marcelo, Choi, YooJung, Monet, Mikaël, Broeck, Guy Van den, Wang, Benjie
This document is an introduction to two related formalisms to define Boolean functions: binary decision diagrams, and Boolean circuits. It presents these formalisms and several of their variants studied in the setting of knowledge compilation. Last,
Externí odkaz:
http://arxiv.org/abs/2404.09674
With the increased use of machine learning systems for decision making, questions about the fairness properties of such systems start to take center stage. Most existing work on algorithmic fairness assume complete observation of features at predicti
Externí odkaz:
http://arxiv.org/abs/2212.02474
Probabilistic circuits (PCs) are a class of tractable probabilistic models that allow efficient, often linear-time, inference of queries such as marginals and most probable explanations (MPE). However, marginal MAP, which is central to many decision-
Externí odkaz:
http://arxiv.org/abs/2111.04833
Publikováno v:
In Composites Science and Technology 18 August 2024 255
Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scenarios for these models that commonly arise in machine learning -- from
Externí odkaz:
http://arxiv.org/abs/2102.06137
Machine learning systems are increasingly being used to make impactful decisions such as loan applications and criminal justice risk assessments, and as such, ensuring fairness of these systems is critical. This is often challenging as the labels in
Externí odkaz:
http://arxiv.org/abs/2009.09031
Decision trees are a popular family of models due to their attractive properties such as interpretability and ability to handle heterogeneous data. Concurrently, missing data is a prevalent occurrence that hinders performance of machine learning mode
Externí odkaz:
http://arxiv.org/abs/2006.16341
Computing expected predictions of discriminative models is a fundamental task in machine learning that appears in many interesting applications such as fairness, handling missing values, and data analysis. Unfortunately, computing expectations of a d
Externí odkaz:
http://arxiv.org/abs/1910.02182
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods often assume a fixed set of observable features to define individuals, b
Externí odkaz:
http://arxiv.org/abs/1906.03843