Tian, Jin

63 publications

TMLR 2025 AlignFix: Fixing Adversarial Perturbations by Agreement Checking for Adversarial Robustness Against Black-Box Attacks Ashutosh Kumar Nirala, Jin Tian, Olukorede Fakorede, Modeste Atsague

NeurIPS 2025 Causal Discovery over Clusters of Variables in Markovian Systems Tara Vafai Anand, Adèle H. Ribeiro, Jin Tian, George Hripcsak, Elias Bareinboim

ICML 2025 Causal Logistic Bandits with Counterfactual Fairness Constraints Jiajun Chen, Jin Tian, Christopher John Quinn

UAI 2025 Decomposition of Probabilities of Causation with Two Mediators Yuta Kawakami, Jin Tian

AISTATS 2025 Graph-Based Complexity for Causal Effect by Empirical Plug-in Rina Dechter, Anna K Raichev, Jin Tian, Alexander Ihler

AAAI 2025 Mediation Analysis for Probabilities of Causation Yuta Kawakami, Jin Tian

UAI 2025 Moments of Causal Effects Yuta Kawakami, Jin Tian

AAAI 2025 Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim

UAI 2024 Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable Yuta Kawakami, Manabu Kuroki, Jin Tian

UAI 2024 Probabilities of Causation for Continuous and Vector Variables Yuta Kawakami, Manabu Kuroki, Jin Tian

TMLR 2024 Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Olukorede Fakorede, Modeste Atsague, Jin Tian

NeurIPS 2024 Unified Covariate Adjustment for Causal Inference Yonghan Jung, Jin Tian, Elias Bareinboim

AAAI 2023 Causal Effect Identification in Cluster DAGs Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim

NeurIPS 2023 Estimating Causal Effects Identifiable from a Combination of Observations and Experiments Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim

ICML 2023 Estimating Joint Treatment Effects by Combining Multiple Experiments Yonghan Jung, Jin Tian, Elias Bareinboim

ICML 2023 Instrumental Variable Estimation of Average Partial Causal Effects Yuta Kawakami, Manabu Kuroki, Jin Tian

TMLR 2023 Vulnerability-Aware Instance Reweighting for Adversarial Training Olukorede Fakorede, Ashutosh Kumar Nirala, Modeste Atsague, Jin Tian

NeurIPS 2022 Finding and Listing Front-Door Adjustment Sets Hyunchai Jeong, Jin Tian, Elias Bareinboim

ICML 2022 Neuron Dependency Graphs: A Causal Abstraction of Neural Networks Yaojie Hu, Jin Tian

ICML 2022 On Measuring Causal Contributions via Do-Interventions Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Bloebaum, Elias Bareinboim

ICML 2022 Partial Counterfactual Identification from Observational and Experimental Data Junzhe Zhang, Jin Tian, Elias Bareinboim

ACML 2021 A Mutual Information Regularization for Adversarial Training Modeste Atsague, Olukorede Fakorede, Jin Tian

NeurIPS 2021 Double Machine Learning Density Estimation for Local Treatment Effects with Instruments Yonghan Jung, Jin Tian, Elias Bareinboim

AAAI 2021 Estimating Identifiable Causal Effects Through Double Machine Learning Yonghan Jung, Jin Tian, Elias Bareinboim

ICML 2021 Estimating Identifiable Causal Effects on Markov Equivalence Class Through Double Machine Learning Yonghan Jung, Jin Tian, Elias Bareinboim

AAAI 2020 Estimating Causal Effects Using Weighting-Based Estimators Yonghan Jung, Jin Tian, Elias Bareinboim

NeurIPS 2020 Learning Causal Effects via Weighted Empirical Risk Minimization Yonghan Jung, Jin Tian, Elias Bareinboim

ICML 2019 Adjustment Criteria for Generalizing Experimental Findings Juan Correa, Jin Tian, Elias Bareinboim

ECML-PKDD 2019 Adjustment Criteria for Recovering Causal Effects from Missing Data Mojdeh Saadati, Jin Tian

AAAI 2019 Identification of Causal Effects in the Presence of Selection Bias Juan D. Correa, Jin Tian, Elias Bareinboim

AAAI 2018 Generalized Adjustment Under Confounding and Selection Biases Juan D. Correa, Jin Tian, Elias Bareinboim

ACML 2017 Recovering Probability Distributions from Missing Data Jin Tian

JMLR 2016 Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling Ru He, Jin Tian, Huaiqing Wu

ACML 2015 Curriculum Learning of Bayesian Network Structures Yanpeng Zhao, Yetian Chen, Kewei Tu, Jin Tian

AISTATS 2015 Exact Bayesian Learning of Ancestor Relations in Bayesian Networks Yetian Chen, Lingjian Meng, Jin Tian

AISTATS 2015 Missing at Random in Graphical Models Jin Tian

AAAI 2015 Recovering Causal Effects from Selection Bias Elias Bareinboim, Jin Tian

AAAI 2014 Finding the K-Best Equivalence Classes of Bayesian Network Structures for Model Averaging Yetian Chen, Jin Tian

UAI 2014 Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, UAI 2014, Quebec City, Quebec, Canada, July 23-27, 2014 Nevin L. Zhang, Jin Tian

AAAI 2014 Recovering from Selection Bias in Causal and Statistical Inference Elias Bareinboim, Jin Tian, Judea Pearl

AAAI 2014 Testable Implications of Linear Structural Equation Models Bryant Chen, Jin Tian, Judea Pearl

NeurIPS 2013 Graphical Models for Inference with Missing Data Karthika Mohan, Judea Pearl, Jin Tian

UAI 2010 Bayesian Model Averaging Using the K-Best Bayesian Network Structures Jin Tian, Ru He, Lavanya Ram

UAI 2009 Computing Posterior Probabilities of Structural Features in Bayesian Networks Jin Tian, Ru He

JMLR 2009 Markov Properties for Linear Causal Models with Correlated Errors Changsung Kang, Jin Tian

IJCAI 2009 Parameter Identification in a Class of Linear Structural Equation Models Jin Tian

UAI 2008 Identifying Dynamic Sequential Plans Jin Tian

UAI 2007 A Criterion for Parameter Identification in Structural Equation Models Jin Tian

AAAI 2007 On the Identification of a Class of Linear Models Jin Tian

UAI 2007 Polynomial Constraints in Causal Bayesian Networks Changsung Kang, Jin Tian

AAAI 2006 A Characterization of Interventional Distributions in Semi-Markovian Causal Models Jin Tian, Changsung Kang, Judea Pearl

UAI 2006 Inequality Constraints in Causal Models with Hidden Variables Changsung Kang, Jin Tian

UAI 2005 Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement Jin Tian

AAAI 2005 Identifying Direct Causal Effects in Linear Models Jin Tian

UAI 2005 Local Markov Property for Models Satisfying Composition Axiom Changsung Kang, Jin Tian

UAI 2004 Identifying Conditional Causal Effects Jin Tian

AAAI 2004 Identifying Linear Causal Effects Jin Tian

AAAI 2002 A General Identification Condition for Causal Effects Jin Tian, Judea Pearl

AAAI 2002 A New Characterization of the Experimental Implications of Causal Bayesian Networks Jin Tian, Judea Pearl

UAI 2002 On the Testable Implications of Causal Models with Hidden Variables Jin Tian, Judea Pearl

UAI 2001 Causal Discovery from Changes Jin Tian, Judea Pearl

UAI 2000 A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks Jin Tian

UAI 2000 Probabilities of Causation: Bounds and Identification Jin Tian, Judea Pearl