
Data Poisoning Attacks and Defenses to Crowdsourcing Systems
A key challenge of big data analytics is how to collect a large volume o...
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Supervised Whole DAG Causal Discovery
We propose to address the task of causal structure learning from data in...
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Adjustment Criteria for Recovering Causal Effects from Missing Data
Confounding bias, missing data, and selection bias are three common obst...
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Purifying Adversarial Perturbation with Adversarially Trained Autoencoders
Machine learning models are vulnerable to adversarial examples. Iterativ...
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Recoverability of Joint Distribution from Missing Data
A probabilistic query may not be estimable from observed data corrupted ...
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Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling
We study the Bayesian model averaging approach to learning Bayesian netw...
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A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks
Exact Bayesian structure discovery in Bayesian networks requires exponen...
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Probabilities of Causation: Bounds and Identification
This paper deals with the problem of estimating the probability that one...
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A BranchandBound Algorithm for MDL Learning Bayesian Networks
This paper extends the work in [Suzuki, 1996] and presents an efficient ...
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Causal Discovery from Changes
We propose a new method of discovering causal structures, based on the d...
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On the Testable Implications of Causal Models with Hidden Variables
The validity OF a causal model can be tested ONLY IF the model imposes c...
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Identifying Conditional Causal Effects
This paper concerns the assessment of the effects of actions from a comb...
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Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
Maximal ancestral graphs (MAGs) are used to encode conditional independe...
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Local Markov Property for Models Satisfying Composition Axiom
The local Markov condition for a DAG to be an independence map of a prob...
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Identifying Dynamic Sequential Plans
We address the problem of identifying dynamic sequential plans in the fr...
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Computing Posterior Probabilities of Structural Features in Bayesian Networks
We study the problem of learning Bayesian network structures from data. ...
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Bayesian Model Averaging Using the kbest Bayesian Network Structures
We study the problem of learning Bayesian network structures from data. ...
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Jin Tian
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Associate Professor of Department of Computer Science at Iowa State University