Michael J. Kearns

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Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells(More)
In this paper we i n vestigate a new formal model of machine learning in which the concept boolean function to be learned may exhibit uncertain or probabilistic behavior|thus, the same input may sometimes be classiied as a positive example and sometimes as a negative example. Such probabilistic concepts or p-concepts m a y arise in situations such a s w(More)
In this paper we describe a new technique for exactly identifying certain classes of read-once Boolean formulas. The method is based on sampling the input-output behavior of the target formula on a probability distribution which is determined by the xed point of the formula's ampliication function (deened as the probability that a 1 is output by the formula(More)
Cells respond to variable environments by changing gene expression and gene interactions. To study how human cells response to stress, we analyzed the expression of >5000 genes in cultured B cells from nearly 100 normal individuals following endoplasmic reticulum stress and exposure to ionizing radiation. We identified thousands of genes that are induced or(More)
Factored models of multiagent systems address the complexity of joint behavior by exploiting locality in agent interactions. History-dependent graphical multiagent models (hGMMs) further capture dynamics by conditioning behavior on history. The challenges of modeling real human behavior motivated us to extend the hGMM representation by distinguishing two(More)