Publications
You may also checkout publications before 2019 and our research overview.
2022
-
ICLRAdaRL: What, Where, and How to Adapt in Transfer Reinforcement LearningIn International Conference on Learning Representations (Spotlight) 2022
-
ICLRLearning Temporally Latent Causal Processes from General Temporal DataIn International Conference on Learning Representations 2022
-
ICLROptimal transport for causal discoveryIn International Conference on Learning Representations (Spotlight) 2022
-
ICLRConditional contrastive learning: Removing undesirable information in self-supervised representationsIn International Conference on Learning Representations 2022
-
ICLRAdversarial robustness through the lens of causalityIn International Conference on Learning Representations 2022
-
CLeaRAttainability and Optimality: The Equalized-Odds Fairness RevisitedIn the first Conference on Causal Learning and Reasoning 2022
-
AIStatsOn the convergence of continuous constrained optimization for structure learningIn International Conference on Artificial Intelligence and Statistics 2022
-
AIStatsTowards Federated Bayesian Network Structure Learning with Continuous OptimizationIn International Conference on Artificial Intelligence and Statistics 2022
-
Pattern RecognitRelevance attack on detectorsPattern Recognition 2022
-
AAAIInvariant Action Effect Model for Reinforcement LearningIn Proceedings of the AAAI conference on Artificial Intelligence 2022
-
AAAIResidual Similarity Based Conditional Independence Test and Its Application in Causal DiscoveryIn Proceedings of the AAAI conference on Artificial Intelligence 2022
-
AAAIIdentification of Linear Latent Variable Model with Arbitrary DistributionIn Proceedings of the AAAI conference on Artificial Intelligence 2022
2021
-
NeurIPSDomain Adaptation with Invariant Representation Learning: What Transformations to Learn?In Conference on Neural Information Processing Systems 2021
-
NeurIPSIdentification of partially observed linear causal models: Graphical conditions for the non-gaussian and heterogeneous casesIn Conference on Neural Information Processing Systems 2021
-
NeurIPSReliable Causal Discovery with Improved Exact Search and Weaker AssumptionsIn Conference on Neural Information Processing Systems 2021
-
NeurIPSInstance-dependent Label-noise Learning under a Structural Causal ModelIn Conference on Neural Information Processing Systems 2021
-
ICCVUnaligned image-to-image translation by learning to reweightIn Proceedings of the International Conference on Computer Vision 2021
-
IJCAIProgressive open-domain response generation with multiple controllable attributesIn Proceedings of the International Joint Conference on Artificial Intelligence 2021
-
TNNLSModel-Based Transfer Reinforcement Learning Based on Graphical Model RepresentationsIEEE Transactions on Neural Networks and Learning Systems 2021
-
TISTCausal Discovery with Confounding Cascade Nonlinear Additive Noise ModelsACM Transactions on Intelligent Systems and Technology 2021
-
TheoriaComputational Causal Discovery: Advantages and Assumptions (Commentary on James Woodward’s paper "Flagpoles anyone?: Causal and explanatory asymmetries")Theoria 2021
-
Neural NetwAdversarial orthogonal regression: Two non-linear regressions for causal inferenceNeural Networks 2021
-
AAAIImproving Causal Discovery By Optimal Bayesian Network LearningIn Proceedings of the AAAI conference on Artificial Intelligence 2021
-
AAAIDeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions EmbeddingIn Proceedings of the AAAI conference on Artificial Intelligence 2021
-
AAAITesting Independence Between Linear Combinations for Causal DiscoveryIn Proceedings of the AAAI Conference on Artificial Intelligence 2021
-
TNNLSCausal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent ConfoundersIEEE Transactions on Neural Networks and Learning Systems 2021
2020
-
NeurIPSDomain adaptation as a problem of inference on graphical modelsIn Conference on Neural Information Processing Systems 2020
-
NeurIPSGeneralized independent noise condition for estimating latent variable causal graphsIn Conference on Neural Information Processing Systems (Spotlight) 2020
-
NeurIPSOn the role of sparsity and dag constraints for learning linear dagsIn Conference on Neural Information Processing Systems 2020
-
NeurIPSHow do fair decisions fare in long-term qualification?In Conference on Neural Information Processing Systems 2020
-
NeurIPSA causal view on robustness of neural networksIn Conference on Neural Information Processing Systems 2020
-
BioinformaticsUnpaired data empowers association testsBioinformatics 2020
-
JMLRCausal Discovery from Heterogeneous/Nonstationary Data.Journal of Machine Learning Research 2020
-
ECCVAdaptive task sampling for meta-learningIn European Conference on Computer Vision 2020
-
ICMLCharacterizing Distribution Equivalence for Cyclic and Acyclic Directed GraphsIn International conference on machine learning 2020
-
ICMLLtf: A label transformation framework for correcting label shiftIn International Conference on Machine Learning 2020
-
ICMLLabel-noise robust domain adaptationIn International Conference on Machine Learning 2020
-
JMLRLearning Linear Non-Gaussian Causal Models in the Presence of Latent Variables.Journal of Machine Learning Research 2020
-
tmcTransfer Learning-Based Outdoor Position Recovery With Cellular DataIEEE Transactions on Mobile Computing 2020
-
AAAICausal discovery from multiple data sets with non-identical variable setsIn Proceedings of the AAAI conference on Artificial Intelligence 2020
-
AAAIGenerative-discriminative complementary learningIn Proceedings of the AAAI Conference on Artificial Intelligence 2020
-
AAAICompressed Self-Attention for Deep Metric LearningIn Proceedings of the AAAI Conference on Artificial Intelligence 2020
2019
-
NeurIPSSpecific and shared causal relation modeling and mechanism-based clusteringIn Conference on Neural Information Processing Systems 2019
-
NeurIPSTriad constraints for learning causal structure of latent variablesIn Conference on Neural Information Processing Systems 2019
-
NeurIPSTwin auxilary classifiers ganIn Conference on Neural Information Processing Systems 2019
-
NeurIPSLikelihood-free overcomplete ICA and applications in causal discoveryIn Conference on Neural Information Processing Systems 2019
-
NeurIPSNeuropathic pain diagnosis simulator for causal discovery algorithm evaluationIn Conference on Neural Information Processing Systems 2019
-
CIKMPrnet: Outdoor position recovery for heterogenous telco data by deep neural networkIn Proceedings of the ACM International Conference on Information and Knowledge Management 2019
-
Front GenetReview of causal discovery methods based on graphical modelsFrontiers in genetics 2019
-
Nat. Commun.Inferring causation from time series in Earth system sciencesNature communications 2019
-
NetwEstimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methodsNetwork Neuroscience 2019
-
Open PhilosThe evaluation of discovery: Models, simulation and search through “big data”Open Philosophy 2019
-
J. Causal InferenceApproximate kernel-based conditional independence tests for fast non-parametric causal discoveryJournal of Causal Inference 2019
-
UAICausal discovery with general non-linear relationships using non-linear icaIn Uncertainty in Artificial Intelligence 2019
-
UAIDomain generalization via multidomain discriminant analysisIn Uncertainty in Artificial Intelligence 2019
-
ICMLCausal discovery and forecasting in nonstationary environments with state-space modelsIn International conference on machine learning 2019
-
ICMLOn learning invariant representations for domain adaptationIn International Conference on Machine Learning 2019
-
IJCAICausal discovery with cascade nonlinear additive noise modelsIn 2019
-
IJCAILearning disentangled semantic representation for domain adaptationIn Proceedings of the International Joint Conference on Artificial Intelligence 2019
-
CVPRGeometry-consistent generative adversarial networks for one-sided unsupervised domain mappingIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (Best Paper Finalist) 2019
-
AIStatsLow-dimensional density ratio estimation for covariate shift correctionIn International Conference on Artificial Intelligence and Statistics 2019
-
AIStatsCausal discovery in the presence of missing dataIn International Conference on Artificial Intelligence and Statistics 2019
-
AIStatsData-driven approach to multiple-source domain adaptationIn International Conference on Artificial Intelligence and Statistics 2019
-
AAAICounting and sampling from Markov equivalent DAGs using clique treesIn Proceedings of the AAAI conference on Artificial Intelligence 2019