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pro vyhledávání: '"Heckerman, David"'
Autor:
Quintas-Martinez, Victor, Bahadori, Mohammad Taha, Santiago, Eduardo, Mu, Jeff, Janzing, Dominik, Heckerman, David
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024
Comparing two samples of data, we observe a change in the distribution of an outcome variable. In the presence of multiple explanatory variables, how much of the change can be explained by each possible cause? We develop a new estimation strategy tha
Externí odkaz:
http://arxiv.org/abs/2404.08839
Autor:
Heckerman, David
This manuscript is technical memoir about my work at Stanford and Microsoft Research. Included are fundamental concepts central to machine learning and artificial intelligence, applications of these concepts, and stories behind their creation.
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Externí odkaz:
http://arxiv.org/abs/2302.05449
We study the problem of observational causal inference with continuous treatments in the framework of inverse propensity-score weighting. To obtain stable weights, we design a new algorithm based on entropy balancing that learns weights to directly m
Externí odkaz:
http://arxiv.org/abs/2107.13068
Autor:
Heckerman, David, Geiger, Dan
We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce several assumptions that permit the construction of likelihoods and paramet
Externí odkaz:
http://arxiv.org/abs/2105.06241
Autor:
Geiger, Dan, Heckerman, David
Publikováno v:
The Annals of Statistics, 30: 1412-1440, 2002
We develop simple methods for constructing parameter priors for model choice among Directed Acyclic Graphical (DAG) models. In particular, we introduce several assumptions that permit the construction of parameter priors for a large number of DAG mod
Externí odkaz:
http://arxiv.org/abs/2105.03248
Concept-based explanation approach is a popular model interpertability tool because it expresses the reasons for a model's predictions in terms of concepts that are meaningful for the domain experts. In this work, we study the problem of the concepts
Externí odkaz:
http://arxiv.org/abs/2007.11500
Autor:
Heckerman, David
Publikováno v:
Original version published in Learning in Graphical Models, M. Jordan, ed., MIT Press, Cambridge, MA, 1999
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model e
Externí odkaz:
http://arxiv.org/abs/2002.00269
Autor:
Heckerman, David
Publikováno v:
Probabilistic Similarity Networks. MIT Press, Cambridge, MA, 1991
Normative expert systems have not become commonplace because they have been difficult to build and use. Over the past decade, however, researchers have developed the influence diagram, a graphical representation of a decision maker's beliefs, alterna
Externí odkaz:
http://arxiv.org/abs/1911.06263
Autor:
Heckerman, David, Meek, Chris
Low-dimensional probability models for local distribution functions in a Bayesian network include decision trees, decision graphs, and causal independence models. We describe a new probability model for discrete Bayesian networks, which we call an em
Externí odkaz:
http://arxiv.org/abs/1910.09715
Autor:
Heckerman, David
Identifying causal relationships from observation data is difficult, in large part, due to the presence of hidden common causes. In some cases, where just the right patterns of conditional independence and dependence lie in the data---for example, Y-
Externí odkaz:
http://arxiv.org/abs/1801.00727