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Causality and machine learning
Causality and machine learning
NEW L. Cohausz, J. Kappenberger, H. Stuckenschmidt. Combining Fairness and Causal Graphs to Advance Both. Proc. 2nd Workshop on Fairness and Bias in AI, pages 1-14, 2024.
[pdf] [Google scholar]H. Yokoyama, R. Shingaki, K. Nishino, S. Shimizu, T. Pham. Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis. Arxiv preprint arXiv: 2411.06990, 2024. Accepted in Proc. IJCNN2025.
[pdf] [Google scholar]D. Zapata, M. Meyer, O. Müller. Combining Causal Discovery and Machine Learning for Modeling Data Center Operations. Arxiv preprint arXiv: 2410.09516, 2024.
[pdf] [Google scholar]D. Takahashi, S. Shimizu, and T. Tanaka. Counterfactual explanations of black-box machine learning models using causal discovery with applications to credit rating. In Proc. Int. Joint Conf. on Neural Networks (IJCNN2024), part of the 2024 IEEE World Congress on Computational Intelligence (WCCI2024), pages 1--8, Yokohama, Japan, 2024.
[pdf] [Google scholar]H. Jiang, L. Ge, Y. Gao, J. Wang, R. Song. LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model. In Proc. First Conference on Language Modeling (COLM2024), pp. xx-xx, 2024.
[pdf] [Google scholar]T. Zhang, C. Wagne. Generating Locally Relevant Explanations Using Causal Rule Discovery. In Proc. 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. xx-xx, 2024.
[pdf] [Google scholar]Z. Xie, Y. Zheng, L. Ottens, K. Zhang, C. Kozyrakis, J. Mace. Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight. Arxiv preprint arXiv:2405.01744, 2024.
[pdf] [Google scholar]E. Khatibi, M. Abbasian, Z. Yang, I. Azimi, A. M. Rahmani. ALCM: Autonomous LLM-Augmented Causal Discovery Framework. Arxiv preprint arXiv:2405.01744, 2024.
[pdf] [Google scholar]R. Pros, J. Vitrià. Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation. Arxiv preprint arXiv:2404.12238, 2024.
[pdf] [Google scholar]P. Li, X. Wang, Z. Zhang, Y. Meng, F. Shen, Y. Li, J. Wang, Y. Li, W. Zhu. LLM-Enhanced Causal Discovery in Temporal Domain from Interventional Data. Arxiv preprint arXiv:2404.14786, 2024.
[pdf] [Google scholar]C. Liu, Y. Chen, T. Liu, M. Gong, J. Cheng, B. Han, K. Zhang. Discovery of the Hidden World with Large Language Models. Arxiv preprint arXiv:2402.03941, 2024.
[pdf] [Google scholar]T. Tse, I. Chan, Z. Chen. Causal Coordinated Concurrent Reinforcement Learning. Arxiv preprint arXiv:2401.18012, 2024.
[pdf] [Google scholar]D. Takahashi, S. Shimizu, T. Tanaka. Counterfactual explanations of black-box machine learning models using causal discovery with applications to credit rating. Arxiv preprint arXiv:2402.02678, 2024.
[pdf] [Google scholar]M. Takayama, T. Okuda, T. Pham, T. Ikenoue, S. Fukuma, S. Shimizu, A. Sannai. Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach. Arxiv preprint arXiv:2402.01454, 2024. Accepted in TMLR.
[pdf] [Google scholar]D. Fahland, F. Fournier, L. Limonad, I. Skarbovsky, A. J.E. Swevels. How well can large language models explain business processes?. Arxiv preprint arXiv:2401.12846, 2024.
[pdf] [Google scholar]R. Moreira, J. Bono, M. Cardoso, P. Saleiro, M. A. T. Figueiredo, P. Bizarro. DiConStruct: Causal Concept-based Explanations through Black-Box Distillation. Arxiv preprint arXiv:2401.08534, 2024.
[pdf] [Google scholar]H. Cai, S. Liu, R. Song. Is Knowledge All Large Language Models Needed for Causal Reasoning?. Arxiv preprint arXiv:2401.00139, 2023.
[pdf] [Google scholar]H. Jiang, L. Ge, Y. Gao, J. Wang, R. Song. Large Language Model for Causal Decision Making. Arxiv preprint arXiv:2312.17122, 2023.
[pdf] [Google scholar]R. Rashid, J. Chowdhury, G. Terejanu. Causal Feature Selection: Methods and a Novel Causal Metric Evaluation Framework. In Proc. 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), , pp. 1--9, 2023.
[pdf] [Google scholar]Z. Fox, A. Ghosh. Active Causal Machine Learning for Molecular Property Prediction. NeurIPS 2023 Workshop: AI for Accelerated Materials Design, 2023.
[pdf] [Google scholar]Z. Fatemi, M. Huynh, E. Zheleva, Z. Syed, X. Di. Mitigating Cold-start Problem Using Cold Causal Demand Forecasting Model. NeurIPS2023 Temporal Graph Learning Workshop, 2023.
[pdf] [Google scholar]Z. Ji, P. Ma, S. Wang, Y. Li. Causality-Aided Trade-off Analysis for Machine Learning Fairness. Arxiv preprint arXiv:2305.13057, 2023.
[pdf] [Google scholar]K. Kiritoshi, T. Izumitani, K. Koyama, T. Okawachi, K. Asahara, and S. Shimizu. Estimating individual-level optimal causal interventions combining causal models and machine learning models. In Proc. KDD'21 Workshop on Causal Discovery, PMLR 150:55-77, 2021.
[pdf] [Google scholar]T. Teshima, I. Sato, M. Sugiyama. Few-shot Domain Adaptation by Causal Mechanism Transfer. Proc. 37th International Conference on Machine Learning (ICML2020), 2020.
[pdf] [Google schlar]K. Zhang, M. Gong, P. Stojanov, B. Huang, C. Glymour. Domain Adaptation As a Problem of Inference on Graphical Models. Arxiv preprint arXiv:2002.03278, 2020.
[pdf] [Google schlar]A. Dhir, C. M. Lee. Integrating overlapping datasets using bivariate causal discovery. In Proc. 34-th AAAI Conference on Artificial Intelligence (AAAI2020), pp. xx--xx, New York, USA, 2020.
[pdf] [Google schlar]T.-L. Nguyen, S. Kavuri, M. Lee. A multimodal convolutional neuro-fuzzy network for emotion understanding of movie clips. Neural Networks, xx: xx-xx, 2019.
[pdf] [Google scholar]P. Blöbaum and S. Shimizu. Estimation of interventional effects of features on prediction. In Proc. 2017 IEEE International Workshop on Machine Learning for Signal Processing (MLSP2017), pp. xx--xx, Tokyo, Japan, 2017.
[pdf] [Python code by T. Ikeuchi and G. Haraoka] [Google scholar]H. Nyberg and P. Saikkonen. Forecasting with a noncausal VAR model. Computational Statistics & Data Analysis, 76: 536-555, 2013.
[pdf] [Google scholar]M. Lanne, J. Luoto and P. Saikkonen. Optimal forecasting of noncausal autoregressive time series. International Journal of Forecasting, 28(3): 623-631, 2012.
[pdf] [Google scholar]B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang and J. Mooij. Semi-supervised learning in causal and anticausal settings. In Empirical Inference, pp. 129-141, 2013.
[pdf] [Google scholar]B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang and J. Mooij. On causal and anticausal learning. In Proc. 29th Int. Conf. on Machine Learning (ICML2012), pp. xx-xx, Edinburgh, Scotland, 2012.
[pdf] [Google scholar]R. E. Tillman and P. Spirtes. When causality matters for prediction: investigating the practical tradeoffs. In JMLR Workshop and Conference Proceedings, Causality: Objectives and Assessment (Proc. NIPS2008 workshop on causality), 6: 137-146, 2010.
[pdf] [videolecture] [Google scholar]
Testing, model fit and reliability
Testing, model fit and reliability
Evaluation of model assumptions
D. Schkoda, M. Drton. Goodness-of-Fit Tests for Linear Non-Gaussian Structural Equation Models. arXiv:2311.04585, 2023.
[pdf] [Google scholar]C. Schultheiss, P. Bühlmann. Assessing the overall and partial causal well-specification of nonlinear additive noise models. arXiv:2310.16502, 2023.
[pdf] [Google scholar]P. M. Faller, L. C. Vankadara, A. A. Mastakouri, F. Locatello, D. Janzing. Self-Compatibility: Evaluating Causal Discovery without Ground Truth. arXiv:2307.09552, 2023.
[pdf] [Google scholar]Y. S. Wang, M. Kolar, M. Drton. Confidence Sets for Causal Orderings. Arxiv preprint arXiv:2305.14506, 2023.
[pdf] [Google schlar]W. Wiedermann and X. Li. Confounder detection in linear mediation models: Performance of kernel-based tests of independence. Behavior Research Methods, xx: xx--xx, 2019.
[pdf] [Google scholar]M. Guerini and A. Moneta. A method for agent-based models validation. LEM WORKING PAPER SERIES: 2016/16, 2016.
[pdf] [Google scholar]D. Entner, P. O. Hoyer and P. Spirtes. Statistical test for consistent estimation of causal effects in linear non-Gaussian models. In JMLR Workshop and Conference Proceedings, AISTATS 2012 (Proc. 15th International Conference on Artificial Intelligence and Statistics), 22: 364-372, 2012.
[pdf] [supplementary] [code] [real data] [Google scholar]D. Entner and P. O. Hoyer. Discovering unconfounded causal relationships using linear non-Gaussian models. New Frontiers in Artificial Intelligence, Lecture Notes in Computer Science, 6797: 181-195, 2011.
[pdf] [code] [Google scholar]K. Ozaki, K. Nakamura and H. Murohashi. A multilevel model using 2nd and 3rd order moments. Proceedings of the Institute of Statistical Mathematics, 58(2): 207--221, 2010. (In Japanese)
[pdf] [Google scholar]S. Shimizu and Y. Kano. Use of non-normality in structural equation modeling: Application to direction of causation. Journal of Statistical Planning and Inference, 138: 3483--3491, 2008.
[pdf] [Google scholar]
Statistical reliability
D. Strieder, M. Drton. Dual Likelihood for Causal Inference under Structure Uncertainty. arXiv preprint arXiv:2402.08328.
[pdf] [Google scholar]D. Strieder, M. Drton. Confidence in Causal Inference under Structure Uncertainty in Linear Causal Models with Equal Variances. arXiv preprint arXiv:2309.04298, 2023.
[pdf] [Google scholar]Y. S. Wang, M. Kolar, M. Drton. Confidence Sets for Causal Orderings. Arxiv preprint arXiv:2305.14506, 2023.
[pdf] [Google schlar]D. Strieder, T. Freidling, S. Haffner, and M. Drton. Confidence in Causal Discovery with Linear Causal Models. In Proc. 37th conference on Uncertainty in Artificial Intelligence (UAI 2021) pp.xx-xx, Online, 2021.
[pdf] [Google scholar]W. Wiedermann, M. Hagmann and A. von Eye. Significance tests to determine the direction of effects in linear regression models. British Journal of Mathematical and Statistical Psychology, 68(1): 116--141, 2015.
[pdf] [Google scholar]K. Thamvitayakul, S. Shimizu, T. Ueno, T. Washio and T. Tashiro. Bootstrap confidence intervals in DirectLiNGAM. In Proc. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW2012), pp.659--668, Brussels, Belgium, 2012.
[pdf] [erratum] [Google scholar]Y. Komatsu, S. Shimizu and H. Shimodaira. Assessing statistical reliability of LiNGAM via multiscale bootstrap. In Proc. International Conference on Artificial Neural Networks (ICANN2010), pp.309-314, Thessaloniki, Greece, 2010.
[pdf] [R code for multiscale bootstrap] [Google scholar]
Learning from multiple datasets
Learning from multiple datasets
K. Jalaldoust, S. Salehkaleybar, N. Kiyavash. MULTI-DOMAIN CAUSAL DISCOVERY WITH BIJECTIVE FIXED-CAUSE FUNCTIONALS. Sharif CausalAI Lab., 2024.
[pdf] [Google scholar]W. Chen, X. Huang, Z. Li, R. Cai, Z. Huang, Z. Hao. Individual Causal Structure Learning from Population Data. In Proc. Thirty-Third International Joint Conference on Artificial Intelligence, pp. 7109-7117, 2024.
[pdf] [Google scholar]V. Malik, K. Bello, A. Ghoshal, J. Honorio. Identifying Causal Changes Between Linear Structural Equation Models. In Proc. 40th Conference on Uncertainty in Artificial Intelligence (UAI2024), 2024.
[pdf] [Google scholar]G. Varando, S. Catsis, E. Diaz, G. Camps-Vallsg. Pairwise causal discovery with support measure machines. Applied Soft Computing, 150:111030, 2023.
[pdf] [Google scholar]M. Ren, X. He, J. Wang. Structural transfer learning of non-Gaussian DAG. arXiv:2310.10239, 2023.
[pdf] [Google scholar]Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto, Z. Hao. Causal Discovery with Multi-Domain LiNGAM for Latent Factors. Arxiv preprint arXiv:2009.09176, 2020.
[pdf] [Google scholar]A. Dhir, C. M. Lee. Integrating overlapping datasets using bivariate causal discovery. In Proc. 34nd AAAI Conference on Artificial Intelligence (AAAI2020), pp. xx-xx, 2020.
[pdf] [Google schlar]B. Huang, K. Zhang, M. Gong, C. Glymour. Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. In Proc. 34th AAAI Conference on Artificial Intelligence (AAAI), pp. xx-xx, 2020.
[pdf] [Google scholar]B. Huang, K. Zhang, P. Xie, M. Gong, E. P. Xing, C. Glymour. Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. In Advances in Neural Information Processing Systems 33 (NIPS2019), pp. xx-xx, 2019.
[pdf] [Google scholar]V. R. López, L. E. S. Succar, F. O. Espina, L. E. Erro. Knowledge Transfer for Learning Subject-Specific Causal Probabilistic Graphical Models. Technical Report No. CCC-19-004, 2019.
[pdf] [Google schlar]L. Xiang, S. Xie, P. McColgan, S. J. Tabrizi, R. I. Scahill, D. Zeng, and Y. Wang. Learning subject-specific directed acyclic graphs with mixed effects structural equation models from observational data. Frontiers in Genetics, 9: 430, 2018.
[pdf] [Google scholar]K. Kadowaki, S. Shimizu and T. Washio. Estimation of causal structures in longitudinal data using non-Gaussianity. In Proc. 23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP2013), pp. 1--6, Southampton, United Kingdom, 2013.
[pdf] [Google scholar]U. Schaechtle, K. Stathis and S. Bromuri. Multi-dimensional causal discovery. In Proc. 23rd International Joint Conference on Artificial Intelligence (IJCAI2013), pp. 1649--1655, Beijing, China, 2013.
[pdf] [Google scholar]S. Shimizu. Joint estimation of linear non-Gaussian acyclic models. Neurocomputing, 81: 104-107, 2012.
[pdf] [Matlab code] [Python code by T. Ikeuchi and G. Haraoka] [Google scholar]J. D. Ramsey, S. J. Hanson and C. Glymour. Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage, 58(3): 838--848, 2011.
[pdf] [TETRAD IV] [Google scholar]
Benchmark datasets
Benchmark datasets
G. Velev, S. Lessmann. Learning Causal Abstractions of Linear Structural Causal Models. arXiv preprint arXiv:2409.19377, 2024.
[pdf] [Google scholar]W. Zhou, H. Huang, G. Zhang, R. Shi, K. Yin, Y. Lin, B. Liu. OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework. arXiv:2406.04598, 2024.
[pdf [Google schlar]R. Tu, Z. Senane, L. Cao, C. Zhang, H. Kjellström, G. E. Henter. Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework. arXiv:2406.08311, 2024.
[pdf] [Google schlar]M. Hardt, W. Orchard, P. Blöbaum, S. Kasiviswanathan, E. Kirschbaum. The PetShop Dataset -- Finding Causes of Performance Issues across Microservices. arXiv:2311.04806, 2023.
[pdf] [Google schlar]S. W. Mogensen, K. Rathsman, P. Nilsson. Causal discovery in a complex industrial system: A time series benchmark. arXiv:2310.18654, 2023.
[pdf] [Google schlar]D. Machlanski, S. Samothrakis, P. Clarke. Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice. arXiv:2310.18212, 2023.
[pdf] [Google schlar]Y. Cheng, Z. Wang, T. Xiao, Q. Zhong, J. Suo, K. He. CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery. arXiv:2310.01753, 2023.
[pdf] [Google schlar]F. Montagna, A. A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello. Assumption violations in causal discovery and the robustness of score matching. arXiv:2310.13387, 2023.
[pdf] [Google schlar]L. Cheng, R. Guo, R. Moraffah, P. Sheth, K. S. Candan, H. Liu. Evaluation Methods and Measures for Causal Learning Algorithms. IEEE Transactions on Artificial Intelligence, xx: xx-xx, 2022.
[pdf] [Google schlar]J. Huegle, C. Hagedorn, L. Böhme, M. Pörschke, J. Umland, R. Schlosser. MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data. Why21 Workshop, 2021.
[pdf] [Google schlar]
Others
Others
NEW M. Dhanakshirur, F. Laumann, J. Park, and M. Barahona. A Continuous Structural Intervention Distance to Compare Causal Graphs. In: Causal Inference. PCIC 2024, 2024.
[pdf] [Google scholar]NEW G. Velev and S. Lessmann. Interpretable, multi-dimensional Evaluation Framework for Causal Discovery from observational i.i.d. Data. IEEE Access, 12: 136502 - 136514, 2024.
[pdf] [Google scholar]NEW T. Li, L. Wang, D. Peng, J. Liao, L. Liu, Z. Liu. Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph. In Causal Inference. PCIC 2024. Communications in Computer and Information Science, vol 2200: 65-73, 2025.
[pdf] [Google scholar]L. Wang, S. Huang, L. Jun, L. Liu. Evaluation Criteria for Causal Discovery Without Ground-Truth Graphs. In Causal Inference. PCIC 2024. Communications in Computer and Information Science, vol 2200: 65-73, 2025.
[pdf] [Google scholar]M. Dhanakshirur, F. Laumann, J. Park. Distance to Compare Causal Graphs. In Causal Inference. PCIC 2024. Communications in Computer and Information Science, vol 2200: 25-40, 2025.
[pdf] [Google scholar]D. Koyuncu, A. Gittens, B. Yener, M. Yung. Adversarial Missingness Attacks on Causal Structure Learning. ACM Transactions on Intelligent Systems and Technology, xx: xx-xx, 2024.
[pdf] [Google scholar]M. Fan, J. Yu, D. Weiskopf, N. Cao, H.-Y. Wang, L. Zhou. Visual Analysis of Multi-outcome Causal Graphs. arXiv preprint arXiv:2408.02679, 2024.
[pdf] [Google scholar]R. Massidda, S. Magliacane, D. Bacciu. Learning Causal Abstractions of Linear Structural Causal Models. arXiv preprint arXiv:2406.00394, 2024.
[pdf] [Google scholar]D. Tramontano, M. Drton, J. Etesami. Parameter identification in linear non-Gaussian causal models under general confounding. arXiv preprint arXiv:2405.20856, 2024.
[pdf] [Google scholar]Q. Ye, A. A. Amini, Q. Zhou. Federated Learning of Generalized Linear Causal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, xx: xx-xx, 2024.
[pdf] [Google scholar]J. Qiao, Z. Chen, J. Yu, R. Cai, Z. Hao. Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model. arXiv:2312.12206, 2023.
[pdf] [Google scholar]S. Gupta, C. Zhang, A. Hilmkil. Learned Causal Method Prediction. arXiv:2311.03989, 2023.
[pdf] [Google scholar]J. R. Loftus, L. E. J. Bynum, S. Hansen. Causal Dependence Plots. arXiv:2303.04209, 2023.
[pdf] [Google scholar]W. Wiedermann, B. Zhang, D. Shi. Detecting heterogeneity in the causal direction of dependence: A model-based recursive partitioning approach. Behavior Research Methods, xx: xx-xx, 2023.
[pdf] [Google scholar]F. Montagna, A. A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello. Assumption violations in causal discovery and the robustness of score matching. Arxiv preprint arXiv:2310.13387, 2024.
[pdf] [Google schlar]C. Schultheiss, P. Bühlmann. On the pitfalls of Gaussian likelihood scoring for causal discovery. Arxiv preprint arXiv:2210.11104, 2022.
[pdf] [Google schlar]E. Kummerfeld, L. Williams, S. Ma. Power Analysis for Causal Discovery. Arxiv preprint arXiv:2112.03555, 2021.
[pdf] [Google schlar]E. Gao, J. Chen, L. Shen, T. Liu, M. Gong, H. Bondell. Federated Causal Discovery. Arxiv preprint arXiv:2112.03555, 2021.
[pdf] [Google schlar]D. Ibeling, T. Icard. A Topological Perspective on Causal Inference. Arxiv preprint arXiv:2107.08558, 2021.
[pdf] [Google schlar]X. Huang, F. Zhu, L. Holloway, A. Haidar. Causal Discovery from Incomplete Data using An Encoder and Reinforcement Learning. Arxiv preprint arXiv:2006.05554, 2020.
[pdf] [Google schlar]Y. Wang, V Menkovski, H Wang, X Du, M Pechenizkiy. Causal Discovery from Incomplete Data: A Deep Learning Approach. Arxiv preprint arXiv:2001.05343, 2020.
[pdf] [Google schlar]M. Peyrard, R. West. A Ladder of Causal Distances. ArXiv preprint arXiv:2005.02480, 2020.
[pdf] [Google schlar]X. Zheng, C. Dan, B. Aragam, P. Ravikumar, E. P. Xing. Learning Sparse Nonparametric DAGs. In Proc. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS2020), Palermo, Sicily, Italy. PMLR: Volume xx..
[pdf] [Google scholar]N. Fei and Y. Yang. An Integrated Causal Path Identification Method. Wuhan University Journal of Natural Sciences, 24(4): 305--313, 2019.
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[pdf] [Google scholar]R. Cai, Z. Zhang, Z. Hao, and M. Winslett. Sophisticated merging over random partitions: a scalable and robust causal discovery approach. IEEE Transactions on Neural Networks and Learning Systems, xx(xx): xx--xx, 2017.
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[pdf] [Google scholar]R. Cai, Z. Zhang and Z. Hao. SADA: A general framework to support robust causation discovery. In JMLR Workshop and Conference Proceedings (Proc. 30th International Conference on Machine Learning, ICML2013), 28(2): 208-216, 2013.
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[pdf] [Google scholar]D. Entner, P. O. Hoyer and P. Spirtes. Statistical test for consistent estimation of causal effects in linear non-Gaussian models. In JMLR Workshop and Conference Proceedings, AISTATS 2012 (Proc. 15th International Conference on Artificial Intelligence and Statistics), 22: 364-372, 2012.
[pdf] [supplementary] [code] [real data] [Google scholar]
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