We consider the problem of inferring the underlying graph using samples from a Markov random field defined on the graph. In particular, we consider the special but interesting case when the underlying graph comes from a distribution on sparse graphs. We provide matching upper and lower bounds for the sample-complexity of learning the underlying graph of a hard-core model, when the underlying gr...

Self-perception theory posits that people understand their own attitudes and preferences much as they understand others', by interpreting the meaning of their behavior in light of the context in which it occurs. Four studies tested whether people also rely on unobservable "behavior," their mindwandering, when making such inferences. It is proposed here that people rely on the content of their m...

Semantic maps are a means of representing universal structure underlying crosslanguage semantic variation. However, no algorithm has existed for inferring a graph-based semantic map from data. Here, we note that this open problem is formally identical to the known problem of inferring a social network from disease outbreaks. From this identity it follows that semantic map inference is computati...

1. Abstract 2. Introduction 3. Computational Approaches for Identifying Gene Modules 3.1. Advanced Statistical Approaches 3.2. Matrix Decomposition Approaches 4. Computational Approaches for Inferring Gene Connectivity 4.1. ODE-based Models 4.2. Bayesian Networks 4.3. Coexpression Networks 4.4. Probabilistic Boolean Networks 4.5. Inference from Multiple Sources of Data 5. Network Analysis in Si...

Linearity and determinism seem to be two essential conditions for polynomial language learning to be possible. We compare several definitions of deterministic linear grammars, and for a reasonable definition prove the existence of a canonical normal form. This enables us to obtain positive learning results in case of polynomial learning from a given set of both positive and negative examples. T...

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observ...

BACKGROUND Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. OBJECTIVE To develop and validate methods of automatically inf...

Inferring evolutionary trees has long been a challenging problem both for biologists and computer scientists. In recent years research has concentrated on the quartet method paradigm for inferring evolutionary trees. Quartet methods proceed by first inferring the evolutionary history for every set of four species (resulting in a set Q of inferred quartet topologies) and then recombining these i...

In this paper, we present an extension of active automata learning to register automata, an automaton model which is capable of expressing the influence of data on control flow. Register automata operate on an infinite data domain, whose values can be assigned to registers and compared for equality. Our active learning algorithm is unique in that it directly infers the effect of data values on ...

Suppose the distribution of the real-valued stationary time series {Xn}n=0 is not known a priori. The goal is to estimate the conditional expectation E(Xn+1|X0, . . . , Xn) from the data segment X0, . . . , Xn such that the difference between the estimate and the conditional expectation should tend to zero almost surely as the number of observations n tends to infinity. This problem (for binary...