Jin Tian - RankMe

87 papers A* 27A 15B 2C 2Misc 2Journal 32Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	B	`conf`	Scalable Tabular Dataset Labeling with Language Models. SSDBM Yaojie Hu, Ilias Fountalis, Jin Tian, Nikolaos Vasiloglou
2025	A*	`conf`	Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies. AAAI Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim
2024	J	`jnl`	AnnotatedTables: A Large Tabular Dataset with Language Model Annotations. CoRR Yaojie Hu, Ilias Fountalis, Jin Tian, Nikolaos Vasiloglou
2024	J	`jnl`	Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies. CoRR Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim
2024	A*	`conf`	Unified Covariate Adjustment for Causal Inference. NeurIPS Yonghan Jung, Jin Tian, Elias Bareinboim
2023	A*	`conf`	Causal Effect Identification in Cluster DAGs. AAAI Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim
2023	J	`jnl`	Codepod: A Namespace-Aware, Hierarchical Jupyter for Interactive Development at Scale. CoRR Hebi Li, Forrest Sheng Bao, Qi Xiao, Jin Tian
2023	A*	`conf`	Estimating Causal Effects Identifiable from a Combination of Observations and Experiments. NeurIPS Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim
2023	A*	`conf`	Estimating Joint Treatment Effects by Combining Multiple Experiments. ICML Yonghan Jung, Jin Tian, Elias Bareinboim
2022	—	`conf`	Circuit Routing Using Monte Carlo Tree Search and Deep Reinforcement Learning. VLSI-DAT Youbiao He, Hebi Li, Jin Tian, Forrest Sheng Bao
2022	J	`jnl`	Effect Identification in Cluster Causal Diagrams. CoRR Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim
2022	A*	`conf`	Finding and Listing Front-door Adjustment Sets. NeurIPS Hyunchai Jeong, Jin Tian, Elias Bareinboim
2022	J	`jnl`	Finding and Listing Front-door Adjustment Sets. CoRR Hyunchai Jeong, Jin Tian, Elias Bareinboim
2022	Misc	`conf`	Group-Wise Feature Selection for Supervised Learning. ICASSP Qi Xiao, Hebi Li, Jin Tian, Zhengdao Wang
2022	A*	`conf`	On Measuring Causal Contributions via do-interventions. ICML Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim
2022	A*	`conf`	Partial Counterfactual Identification from Observational and Experimental Data. ICML Junzhe Zhang, Jin Tian, Elias Bareinboim
2022	—	`ch.`	Recovering from Selection Bias in Causal and Statistical Inference. Probabilistic and Causal Inference Elias Bareinboim, Jin Tian, Judea Pearl
2021	—	`conf`	An Improved (Adversarial) Reprogramming Technique for Neural Networks. ICANN (1) Eliska Kloberdanz, Jin Tian, Wei Le
2021	A*	`conf`	BHDL: A Lucid, Expressive, and Embedded Programming Language and System for PCB Designs. DAC Hebi Li, Youbiao He, Qi Xiao, Jin Tian, Forrest Sheng Bao
2021	A*	`conf`	Data Poisoning Attacks and Defenses to Crowdsourcing Systems. WWW Minghong Fang, Minghao Sun, Qi Li, Neil Zhenqiang Gong, Jin Tian, Jia Liu
2021	J	`jnl`	Data Poisoning Attacks and Defenses to Crowdsourcing Systems. CoRR Minghong Fang, Minghao Sun, Qi Li, Neil Zhenqiang Gong, Jin Tian, Jia Liu
2021	A*	`conf`	Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. NeurIPS Yonghan Jung, Jin Tian, Elias Bareinboim
2021	A*	`conf`	Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. ICML Yonghan Jung, Jin Tian, Elias Bareinboim
2021	A*	`conf`	Estimating Identifiable Causal Effects through Double Machine Learning. AAAI Yonghan Jung, Jin Tian, Elias Bareinboim
2021	J	`jnl`	Partial Counterfactual Identification from Observational and Experimental Data. CoRR Junzhe Zhang, Jin Tian, Elias Bareinboim
2020	A*	`conf`	Estimating Causal Effects Using Weighting-Based Estimators. AAAI Yonghan Jung, Jin Tian, Elias Bareinboim
2020	A*	`conf`	Learning Causal Effects via Weighted Empirical Risk Minimization. NeurIPS Yonghan Jung, Jin Tian, Elias Bareinboim
2020	J	`jnl`	Supervised Whole DAG Causal Discovery. CoRR Hebi Li, Qi Xiao, Jin Tian
2019	A*	`conf`	Adjustment Criteria for Generalizing Experimental Findings. ICML Juan D. Correa, Jin Tian, Elias Bareinboim
2019	—	`conf`	Adjustment Criteria for Recovering Causal Effects from Missing Data. ECML/PKDD (1) Mojdeh Saadati, Jin Tian
2019	J	`jnl`	Adjustment Criteria for Recovering Causal Effects from Missing Data. CoRR Mojdeh Saadati, Jin Tian
2019	J	`jnl`	Event prediction algorithm using neural networks for the power management system of electric vehicles. Appl. Soft Comput. Ki-sung Koo, Manimaran Govindarasu, Jin Tian
2019	A*	`conf`	Identification of Causal Effects in the Presence of Selection Bias. AAAI Juan D. Correa, Jin Tian, Elias Bareinboim
2019	J	`jnl`	Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders. CoRR Hebi Li, Qi Xiao, Shixin Tian, Jin Tian
2018	A*	`conf`	Generalized Adjustment Under Confounding and Selection Biases. AAAI Juan D. Correa, Jin Tian, Elias Bareinboim
2017	J	`jnl`	Learning Bayesian network structures under incremental construction curricula. Neurocomputing Yanpeng Zhao, Yetian Chen, Kewei Tu, Jin Tian
2017	C	`conf`	Recovering Probability Distributions from Missing Data. ACML Jin Tian
2016	B	`conf`	Joint Discovery of Skill Prerequisite Graphs and Student Models. EDM Yetian Chen, José P. González-Brenes, Jin Tian
2016	J	`jnl`	Recoverability of Joint Distribution from Missing Data. CoRR Jin Tian
2016	J	`jnl`	Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling. J. Mach. Learn. Res. Ru He, Jin Tian, Huaiqing Wu
2015	C	`conf`	Curriculum Learning of Bayesian Network Structures. ACML Yanpeng Zhao, Yetian Chen, Kewei Tu, Jin Tian
2015	A	`conf`	Exact Bayesian Learning of Ancestor Relations in Bayesian Networks. AISTATS Yetian Chen, Lingjian Meng, Jin Tian
2015	A	`conf`	Missing at Random in Graphical Models. AISTATS Jin Tian
2015	A*	`conf`	Recovering Causal Effects from Selection Bias. AAAI Elias Bareinboim, Jin Tian
2015	J	`jnl`	Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling. CoRR Ru He, Jin Tian, Huaiqing Wu
2014	J	`jnl`	A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks. CoRR Yetian Chen, Jin Tian, Olga Nikolova, Srinivas Aluru
2014	A*	`conf`	Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging. AAAI Yetian Chen, Jin Tian
2014	A*	`conf`	Recovering from Selection Bias in Causal and Statistical Inference. AAAI Elias Bareinboim, Jin Tian, Judea Pearl
2014	A*	`conf`	Testable Implications of Linear Structural Equation Models. AAAI Bryant Chen, Jin Tian, Judea Pearl
2013	J	`jnl`	A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks CoRR Jin Tian
2013	J	`jnl`	Causal Discovery from Changes CoRR Jin Tian, Judea Pearl
2013	—	`conf`	Graphical Models for Inference with Missing Data. NIPS Karthika Mohan, Judea Pearl, Jin Tian
2013	J	`jnl`	On the Testable Implications of Causal Models with Hidden Variables CoRR Jin Tian, Judea Pearl
2013	J	`jnl`	Probabilities of Causation: Bounds and Identification CoRR Jin Tian, Judea Pearl
2013	J	`jnl`	Session analysis of people search within a professional social network. J. Assoc. Inf. Sci. Technol. Ru He, Jiong Wang, Jin Tian, Cheng-Tao Chu, Bradley Mauney, Igor Perisic
2012	J	`jnl`	A Criterion for Parameter Identification in Structural Equation Models CoRR Jin Tian
2012	J	`jnl`	Bayesian Model Averaging Using the k-best Bayesian Network Structures CoRR Jin Tian, Ru He, Lavanya Ram
2012	J	`jnl`	Computing Posterior Probabilities of Structural Features in Bayesian Networks CoRR Jin Tian, Ru He
2012	J	`jnl`	Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement CoRR Jin Tian
2012	J	`jnl`	Identifying Conditional Causal Effects CoRR Jin Tian
2012	J	`jnl`	Identifying Dynamic Sequential Plans CoRR Jin Tian
2012	J	`jnl`	Inequality Constraints in Causal Models with Hidden Variables. CoRR Changsung Kang, Jin Tian
2012	J	`jnl`	Local Markov Property for Models Satisfying Composition Axiom CoRR Changsung Kang, Jin Tian
2012	J	`jnl`	Polynomial Constraints in Causal Bayesian Networks CoRR Changsung Kang, Jin Tian
2010	A	`conf`	Bayesian Model Averaging Using the k-best Bayesian Network Structures. UAI Jin Tian, Ru He, Lavanya Ram
2009	A	`conf`	Computing Posterior Probabilities of Structural Features in Bayesian Networks. UAI Jin Tian, Ru He
2009	J	`jnl`	Markov Properties for Linear Causal Models with Correlated Errors. J. Mach. Learn. Res. Changsung Kang, Jin Tian
2009	A*	`conf`	Parameter Identification in a Class of Linear Structural Equation Models. IJCAI Jin Tian
2008	A	`conf`	Identifying Dynamic Sequential Plans. UAI Jin Tian
2007	A	`conf`	A Criterion for Parameter Identification in Structural Equation Models. UAI Jin Tian
2007	A*	`conf`	On the Identification of a Class of Linear Models. AAAI Jin Tian
2007	A	`conf`	Polynomial Constraints in Causal Bayesian Networks. UAI Changsung Kang, Jin Tian
2006	A*	`conf`	A Characterization of Interventional Distributions in Semi-Markovian Causal Models. AAAI Jin Tian, Changsung Kang, Judea Pearl
2006	Misc	`conf`	A Hybrid Generative/Discriminative Bayesian Classifier. FLAIRS Changsung Kang, Jin Tian
2006	A	`conf`	Inequality Constraints in Causal Models with Hidden Variables. UAI Changsung Kang, Jin Tian
2005	A	`conf`	Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement. UAI Jin Tian
2005	A*	`conf`	Identifying Direct Causal Effects in Linear Models. AAAI Jin Tian
2005	A	`conf`	Local Markov Property for Models Satisfying Composition Axiom. UAI Changsung Kang, Jin Tian
2004	A	`conf`	Identifying Conditional Causal Effects. UAI Jin Tian
2004	A*	`conf`	Identifying Linear Causal Effects. AAAI Jin Tian
2002	—	`conf`	A General Identification Condition for Causal Effects. AAAI/IAAI Jin Tian, Judea Pearl
2002	—	`conf`	A New Characterization of the Experimental Implications of Causal Bayesian Networks. AAAI/IAAI Jin Tian, Judea Pearl
2002	A	`conf`	On the Testable Implications of Causal Models with Hidden Variables. UAI Jin Tian, Judea Pearl
2001	A	`conf`	Causal Discovery from Changes. UAI Jin Tian, Judea Pearl
2000	A	`conf`	A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks. UAI Jin Tian
2000	A	`conf`	Probabilities of Causation: Bounds and Identification. UAI Jin Tian, Judea Pearl
2000	J	`jnl`	Probabilities of causation: Bounds and identification. Ann. Math. Artif. Intell. Jin Tian, Judea Pearl