Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Chajewska, Urszula"'
Autor:
Chajewska, Urszula, Shrivastava, Harsh
Federated Learning (FL) addresses the need to create models based on proprietary data in such a way that multiple clients retain exclusive control over their data, while all benefit from improved model accuracy due to pooled resources. Recently propo
Externí odkaz:
http://arxiv.org/abs/2309.11680
Autor:
Chajewska, Urszula, Shrivastava, Harsh
Conditional Independence (CI) graph is a special type of a Probabilistic Graphical Model (PGM) where the feature connections are modeled using an undirected graph and the edge weights show the partial correlation strength between the features. Since
Externí odkaz:
http://arxiv.org/abs/2308.05857
Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large impact on the resulting machine learning model. In medical application
Externí odkaz:
http://arxiv.org/abs/2304.11749
Autor:
Shrivastava, Harsh, Chajewska, Urszula
Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries. This limits their adoption to only identifying connections among the input variables. On the
Externí odkaz:
http://arxiv.org/abs/2302.13582
Autor:
Shrivastava, Harsh, Chajewska, Urszula
Publikováno v:
Journal of Artificial Intelligence Research 80 (2024) 593-612
Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives informatio
Externí odkaz:
http://arxiv.org/abs/2211.06829
Autor:
Shrivastava, Harsh, Chajewska, Urszula
Probabilistic Graphical Models are often used to understand dynamics of a system. They can model relationships between features (nodes) and the underlying distribution. Theoretically these models can represent very complex dependency functions, but i
Externí odkaz:
http://arxiv.org/abs/2210.00453
Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models are used for
Externí odkaz:
http://arxiv.org/abs/2205.11610
Rapid progress in representation learning has led to a proliferation of embedding models, and to associated challenges of model selection and practical application. It is non-trivial to assess a model's generalizability to new, candidate datasets and
Externí odkaz:
http://arxiv.org/abs/2202.02339
Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions while still remaining interpretable. In the first part of this paper, we generalize a state
Externí odkaz:
http://arxiv.org/abs/1810.09092
Autor:
Chajewska, Urszula, Halpern, Joseph Y.
As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations.
Externí odkaz:
http://arxiv.org/abs/1302.1526