We stand with Ukraine

We stand with Ukraine

Jin Tian

Orcid: 0000-0001-5313-1600

Affiliations:

Iowa State University, Department of Computer Science, Ames, IA, USA
University of California, Los Angeles, Computer Science Department, CA, USA

According to our database¹, Jin Tian authored at least 64 papers between 2000 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

Online presence:

On csauthors.net:

Bibliography

2024

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies.

[BibT_eX]

[DOI]

,

,

,

Elias Bareinboim

CoRR, 2024

2023

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

[BibT_eX]

[DOI]

,

Forrest Sheng Bao

,

,

CoRR, 2023

Estimating Causal Effects Identifiable from a Combination of Observations and Experiments.

[BibT_eX]

[DOI]

,

,

,

Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Estimating Joint Treatment Effects by Combining Multiple Experiments.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2023

Causal Effect Identification in Cluster DAGs.

[BibT_eX]

[DOI]

,

Adèle H. Ribeiro

,

,

Elias Bareinboim

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Effect Identification in Cluster Causal Diagrams.

[BibT_eX]

[DOI]

,

Adèle H. Ribeiro

,

,

Elias Bareinboim

CoRR, 2022

Circuit Routing Using Monte Carlo Tree Search and Deep Reinforcement Learning.

[BibT_eX]

[DOI]

,

,

,

Forrest Sheng Bao

Proceedings of the 2022 International Symposium on VLSI Design, Automation and Test, 2022

Finding and Listing Front-door Adjustment Sets.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Partial Counterfactual Identification from Observational and Experimental Data.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2022

On Measuring Causal Contributions via do-interventions.

[BibT_eX]

[DOI]

,

Shiva Prasad Kasiviswanathan

,

,

Dominik Janzing

,

Patrick Blöbaum

,

Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2022

Group-Wise Feature Selection for Supervised Learning.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the IEEE International Conference on Acoustics, 2022

Recovering from Selection Bias in Causal and Statistical Inference.

[BibT_eX]

[DOI]

Elias Bareinboim

,

,

Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

2021

Data Poisoning Attacks and Defenses to Crowdsourcing Systems.

[BibT_eX]

[DOI]

,

,

,

Neil Zhenqiang Gong

,

,

Proceedings of the WWW '21: The Web Conference 2021, 2021

Double Machine Learning Density Estimation for Local Treatment Effects with Instruments.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the 38th International Conference on Machine Learning, 2021

An Improved (Adversarial) Reprogramming Technique for Neural Networks.

[BibT_eX]

[DOI]

Eliska Kloberdanz

,

,

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

BHDL: A Lucid, Expressive, and Embedded Programming Language and System for PCB Designs.

[BibT_eX]

[DOI]

,

,

,

,

Forrest Sheng Bao

Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021

Estimating Identifiable Causal Effects through Double Machine Learning.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Supervised Whole DAG Causal Discovery.

[BibT_eX]

[DOI]

,

,

CoRR, 2020

Learning Causal Effects via Weighted Empirical Risk Minimization.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Estimating Causal Effects Using Weighting-Based Estimators.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2019

Event prediction algorithm using neural networks for the power management system of electric vehicles.

[BibT_eX]

[DOI]

,

Manimaran Govindarasu

,

Appl. Soft Comput., 2019

Adjustment Criteria for Recovering Causal Effects from Missing Data.

[BibT_eX]

[DOI]

,

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Adjustment Criteria for Generalizing Experimental Findings.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the 36th International Conference on Machine Learning, 2019

Identification of Causal Effects in the Presence of Selection Bias.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Generalized Adjustment Under Confounding and Selection Biases.

[BibT_eX]

[DOI]

,

,

Elias Bareinboim

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Learning Bayesian network structures under incremental construction curricula.

[BibT_eX]

[DOI]

,

,

,

Neurocomputing, 2017

Recovering Probability Distributions from Missing Data.

[BibT_eX]

[DOI]

Proceedings of The 9th Asian Conference on Machine Learning, 2017

2016

Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling.

[BibT_eX]

[DOI]

,

,

J. Mach. Learn. Res., 2016

Recoverability of Joint Distribution from Missing Data.

[BibT_eX]

[DOI]

CoRR, 2016

Joint Discovery of Skill Prerequisite Graphs and Student Models.

[BibT_eX]

[DOI]

,

José P. González-Brenes

,

Proceedings of the 9th International Conference on Educational Data Mining, 2016

2015

Missing at Random in Graphical Models.

[BibT_eX]

[DOI]

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Exact Bayesian Learning of Ancestor Relations in Bayesian Networks.

[BibT_eX]

[DOI]

,

,

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Curriculum Learning of Bayesian Network Structures.

[BibT_eX]

[DOI]

,

,

,

Proceedings of The 7th Asian Conference on Machine Learning, 2015

Recovering Causal Effects from Selection Bias.

[BibT_eX]

[DOI]

Elias Bareinboim

,

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2014

Testable Implications of Linear Structural Equation Models.

[BibT_eX]

[DOI]

,

,

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging.

[BibT_eX]

[DOI]

,

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

Recovering from Selection Bias in Causal and Statistical Inference.

[BibT_eX]

[DOI]

Elias Bareinboim

,

,

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Session analysis of people search within a professional social network.

[BibT_eX]

[DOI]

,

,

,

,

,

J. Assoc. Inf. Sci. Technol., 2013

Graphical Models for Inference with Missing Data.

[BibT_eX]

[DOI]

,

,

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2010

Bayesian Model Averaging Using the k-best Bayesian Network Structures.

[BibT_eX]

[DOI]

,

,

Proceedings of the UAI 2010, 2010

2009

Markov Properties for Linear Causal Models with Correlated Errors.

[BibT_eX]

[DOI]

,

J. Mach. Learn. Res., 2009

Computing Posterior Probabilities of Structural Features in Bayesian Networks.

[BibT_eX]

[DOI]

,

Proceedings of the UAI 2009, 2009

Parameter Identification in a Class of Linear Structural Equation Models.

[BibT_eX]

[DOI]

Proceedings of the IJCAI 2009, 2009

2008

Identifying Dynamic Sequential Plans.

[BibT_eX]

[DOI]

Proceedings of the UAI 2008, 2008

2007

A Criterion for Parameter Identification in Structural Equation Models.

[BibT_eX]

[DOI]

Proceedings of the UAI 2007, 2007

Polynomial Constraints in Causal Bayesian Networks.

[BibT_eX]

[DOI]

,

Proceedings of the UAI 2007, 2007

On the Identification of a Class of Linear Models.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006

Inequality Constraints in Causal Models with Hidden Variables.

[BibT_eX]

[DOI]

,

Proceedings of the UAI '06, 2006

A Hybrid Generative/Discriminative Bayesian Classifier.

[BibT_eX]

[DOI]

,

Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, 2006

A Characterization of Interventional Distributions in Semi-Markovian Causal Models.

[BibT_eX]

[DOI]

,

,

Proceedings of the Proceedings, 2006

2005

Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement.

[BibT_eX]

[DOI]

Proceedings of the UAI '05, 2005

Local Markov Property for Models Satisfying Composition Axiom.

[BibT_eX]

[DOI]

,

Proceedings of the UAI '05, 2005

Identifying Direct Causal Effects in Linear Models.

[BibT_eX]

[DOI]

Proceedings of the Proceedings, 2005

2004

Identifying Conditional Causal Effects.

[BibT_eX]

[DOI]

Proceedings of the UAI '04, 2004

Identifying Linear Causal Effects.

[BibT_eX]

[DOI]

Proceedings of the Nineteenth National Conference on Artificial Intelligence, 2004

2002

On the Testable Implications of Causal Models with Hidden Variables.

[BibT_eX]

[DOI]

,

Proceedings of the UAI '02, 2002

A New Characterization of the Experimental Implications of Causal Bayesian Networks.

[BibT_eX]

[DOI]

,

Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28, 2002

A General Identification Condition for Causal Effects.

[BibT_eX]

[DOI]

,

Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28, 2002

2001

Causal Discovery from Changes.

[BibT_eX]

[DOI]

,

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

2000

Probabilities of causation: Bounds and identification.

[BibT_eX]

[DOI]

,

Ann. Math. Artif. Intell., 2000

A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks.

[BibT_eX]

[DOI]

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

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