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07/14/2023
Vulnerability-Aware Instance Reweighting For Adversarial Training
Adversarial Training (AT) has been found to substantially improve the ro...
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03/15/2023
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations
Adversarial training (AT) methods have been found to be effective agains...
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01/06/2023
Codepod: A Namespace-Aware, Hierarchical Jupyter for Interactive Development at Scale
Jupyter is a browser-based interactive development environment that has ...
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10/11/2022
Finding and Listing Front-door Adjustment Sets
Identifying the effects of new interventions from data is a significant ...
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02/22/2022
Effect Identification in Cluster Causal Diagrams
One pervasive task found throughout the empirical sciences is to determi...
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10/12/2021
Partial Counterfactual Identification from Observational and Experimental Data
This paper investigates the problem of bounding counterfactual queries f...
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09/21/2021
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Local decision rules are commonly understood to be more explainable, due...
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02/18/2021
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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06/08/2020
Supervised Whole DAG Causal Discovery
We propose to address the task of causal structure learning from data in...
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07/02/2019
Adjustment Criteria for Recovering Causal Effects from Missing Data
Confounding bias, missing data, and selection bias are three common obst...
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05/26/2019
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders
Machine learning models are vulnerable to adversarial examples. Iterativ...
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11/15/2016
Recoverability of Joint Distribution from Missing Data
A probabilistic query may not be estimable from observed data corrupted ...
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01/19/2015
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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08/07/2014
A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks
Exact Bayesian structure discovery in Bayesian networks requires exponen...
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01/16/2013
Probabilities of Causation: Bounds and Identification
This paper deals with the problem of estimating the probability that one...
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01/16/2013
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
This paper extends the work in [Suzuki, 1996] and presents an efficient ...
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01/10/2013
Causal Discovery from Changes
We propose a new method of discovering causal structures, based on the d...
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12/12/2012
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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07/11/2012
Identifying Conditional Causal Effects
This paper concerns the assessment of the effects of actions from a comb...
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07/04/2012
Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
Maximal ancestral graphs (MAGs) are used to encode conditional independe...
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07/04/2012
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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06/13/2012
Identifying Dynamic Sequential Plans
We address the problem of identifying dynamic sequential plans in the fr...
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05/09/2012
Computing Posterior Probabilities of Structural Features in Bayesian Networks
We study the problem of learning Bayesian network structures from data. ...
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03/15/2012
