Jin Tian | DeepAI

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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...

0 Olukorede Fakorede, et al. ∙

∙ 03/15/2023

Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations

Adversarial training (AT) methods have been found to be effective agains...

0 Olukorede Fakorede, et al. ∙

∙ 01/06/2023

Codepod: A Namespace-Aware, Hierarchical Jupyter for Interactive Development at Scale

Jupyter is a browser-based interactive development environment that has ...

0 Hebi Li, et al. ∙

∙ 10/11/2022

Finding and Listing Front-door Adjustment Sets

Identifying the effects of new interventions from data is a significant ...

0 Hyunchai Jeong, et al. ∙

∙ 02/22/2022

Effect Identification in Cluster Causal Diagrams

One pervasive task found throughout the empirical sciences is to determi...

0 Tara V. Anand, et al. ∙

∙ 10/12/2021

Partial Counterfactual Identification from Observational and Experimental Data

This paper investigates the problem of bounding counterfactual queries f...

0 Junzhe Zhang, et al. ∙

∙ 09/21/2021

Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles

Local decision rules are commonly understood to be more explainable, due...

7 Xin Du, et al. ∙

∙ 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...

0 Minghong Fang, et al. ∙

∙ 06/08/2020

Supervised Whole DAG Causal Discovery

We propose to address the task of causal structure learning from data in...

0 Hebi Li, et al. ∙

∙ 07/02/2019

Adjustment Criteria for Recovering Causal Effects from Missing Data

Confounding bias, missing data, and selection bias are three common obst...

0 Mojdeh Saadati, et al. ∙

∙ 05/26/2019

Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders

Machine learning models are vulnerable to adversarial examples. Iterativ...

0 Hebi Li, et al. ∙

∙ 11/15/2016

Recoverability of Joint Distribution from Missing Data

A probabilistic query may not be estimable from observed data corrupted ...

0 Jin Tian, et al. ∙

∙ 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...

0 Ru He, et al. ∙

∙ 08/07/2014

A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks

Exact Bayesian structure discovery in Bayesian networks requires exponen...

0 Yetian Chen, et al. ∙

∙ 01/16/2013

Probabilities of Causation: Bounds and Identification

This paper deals with the problem of estimating the probability that one...

0 Jin Tian, et al. ∙

∙ 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 ...

0 Jin Tian, et al. ∙

∙ 01/10/2013

Causal Discovery from Changes

We propose a new method of discovering causal structures, based on the d...

0 Jin Tian, et al. ∙

∙ 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...

0 Jin Tian, et al. ∙

∙ 07/11/2012

Identifying Conditional Causal Effects

This paper concerns the assessment of the effects of actions from a comb...

0 Jin Tian, et al. ∙

∙ 07/04/2012

Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement

Maximal ancestral graphs (MAGs) are used to encode conditional independe...

0 Jin Tian, et al. ∙

∙ 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...

0 Changsung Kang, et al. ∙

∙ 06/13/2012

Identifying Dynamic Sequential Plans

We address the problem of identifying dynamic sequential plans in the fr...

0 Jin Tian, et al. ∙

∙ 05/09/2012

Computing Posterior Probabilities of Structural Features in Bayesian Networks

We study the problem of learning Bayesian network structures from data. ...

0 Jin Tian, et al. ∙

∙ 03/15/2012

Bayesian Model Averaging Using the k-best Bayesian Network Structures

We study the problem of learning Bayesian network structures from data. ...

0 Jin Tian, et al. ∙

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