Book Reading: Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009. 

1. [Book Reading] Probabilistic Graphical Models: principles and techniques.  [Ch17 - Ch20]

2. [Book Reading] Pattern Recognition and Machine Learning [Ch8]

3. [Paper Review] Identifiability of causal graphs under nonadditive conditionally parametric causal models [Slide], [Video] 

4. [Paper Review] Kim, Hyunsung, and Yaeji Lim. "Functional clustering on a sphere via Riemannian functional principal components." Stat 12.1 (2023): e557.  [Slide], [Video] 

5. [Paper Review] Li, S., Cai, T. T., & Li, H. (2022). Transfer learning in large-scale gaussian graphical models with false discovery rate control. Journal of the American Statistical Association, 1-13. [Slide], [Video] 

6. [Paper Review] Xie, Feng, et al. "Generalized independent noise condition for estimating latent variable causal graphs." Advances in neural information processing systems 33 (2020): 14891-14902. [Slide], [Video] 

7. [Paper Review] Awan, J., & Slavković, A. (2021). Structure and sensitivity in differential privacy: Comparing k-norm mechanisms. Journal of the American Statistical Association, 116(534), 935-954. [Slide], [Video] 

8. [Paper Review] Yuan, M., and Lin, Y. (2007), “Model Selection and Estimation in Regression With Grouped Variables,” Journal of the Royal Statistical Society, Series B, 68, 49–67. [Slide], [Video] 

9. [Paper Review] Positivity in Linear Gaussian Structural Equation Models [Slide], [Video] 

10. [Paper Review] TopHeat: Ecient learning of DAG Structures in Heavy-tailed Data [Slide], [Video] 

11. [Paper Review] Kalisch, Markus, and Peter Bühlman. "Estimating high-dimensional directed acyclic graphs with the PC-algorithm." Journal of Machine Learning Research 8.3 (2007).[Slide], [Video] 

12. [Paper Review] Shimizu, Shohei. "LiNGAM: Non-Gaussian methods for estimating causal structures." Behaviormetrika 41.1 (2014): 65-98.  [Slide], [Video] 

13. [Paper Review] Shimizu, Shohei, et al. "DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model." Journal of Machine Learning Research-JMLR 12.Apr (2011): 1225-1248. [Slide], [Video] 

14. [Paper Review] Saeed, Basil, et al. "Anchored causal inference in the presence of measurement error." Conference on uncertainty in artificial intelligence. PMLR, 2020. [Slide], [Video] 

15. [Paper Review] Zhao, Sihai Dave, T. Tony Cai, and Hongzhe Li. "Direct estimation of differential networks." Biometrika 101.2 (2014): 253-268. [Slide], [Video] 

16. [Paper Review] Liu, Song, et al. "Support consistency of direct sparse-change learning in Markov networks." The Annals of Statistics 45.3 (2017): 959-990. [Slide], [Video] 

17. [Paper Review] Yuan, Huili, et al. "Differential network analysis via lasso penalized D-trace loss." Biometrika 104.4 (2017): 755-770.  [Slide], [Video] 

18. [Paper Review] Fazayeli, Farideh, and Arindam Banerjee. "Generalized direct change estimation in ising model structure." International Conference on Machine Learning. PMLR, 2016. [Slide], [Video] 

19. [Paper Review] Balmand, Samuel, and Arnak S. Dalalyan. "On estimation of the diagonal elements of a sparse precision matrix." Electronic Journal of Statistics 10.1 (2016): 1551-1579. [Slide], [Video] 

1. [Book Reading] Bayesian networks with example in R. [Ch1 - Ch4] 

2. [Book Reading] Probabilistic Graphical Models: principles and techniques [Ch1 - Ch8] 

3. [Paper Review] Peters, Jonas, and Peter Bühlmann. "Identifiability of Gaussian structural equation models with equal error variances." Biometrika 101.1 (2014): 219-228. [Slide], [Video] 

4. [Paper Review] Park, Gunwoong. "Identifiability of Additive Noise Models Using Conditional Variances" Journal of Machine Learning Research 21.75 (2020): 1-34. [Slide], [Video] 

5. [Paper Review] Park, Gunwoong and Youngwhan Kim."Identifiability of Gaussian Structural Equation Models with Homogeneous and Heterogeneous Error Variances" Journal of the Korean Statistical Society 49.1 (2020): 276-292. [Slide], [Video] 

6. [Paper Review] Ghoshal, Asish, and Jean Honorio. "Learning linear structural equation models in polynomial time and sample complexity." International Conference on Artificial Intelligence and Statistics. PMLR, 2018. [Slide], [Video] 

7. [Paper Review] Chen, Wenyu, Mathias Drton, and Y. Samuel Wang. "On causal discovery with an equal-variance assumption." Biometrika 106.4 (2019): 973-980. [Slide], [Video] 

8. [Paper Review] Park, Gunwoong, et al. "Learning a high-dimensional linear structural equation model via l1-regularized regression." Journal of Machine Learning Research 22.102 (2021): 1-41. [Slide], [Video] 

9. [Paper Review] Gan, Lingrui, Naveen N. Narisetty, and Feng Liang. "Bayesian regularization for graphical models with unequal shrinkage." Journal of the American Statistical Association 114.527 (2019): 1218-1231. [Slide], [Video] 

10. [Paper Review] Gao, Ming, Wai Ming Tai, and Bryon Aragam. "Optimal estimation of Gaussian DAG models." International Conference on Artificial Intelligence and Statistics. PMLR, 2022. [Slide], [Video] 

11. [Paper Review] Loh, Po-Ling, and Peter Buhlmann. "High-dimensional learning of linear causal networks via inverse covariance estimation." The Journal of Machine Learning Research 15.1 (2014): 3065-3105. [Slide], [Video] 

12. [Paper Review] Loh, Po-Ling, and Martin J. Wainwright. "Support recovery without incoherence: A case for nonconvex regularization." The Annals of Statistics 45.6 (2017): 2455-2482. [Slide], [Video] 

13. [Paper Review] Cai, T., Liu, W., and Luo, X. (2011), “A Constrained l1 Minimization Approach to Sparse PrecisionMatrix Estimation,” Journal of the American Statistical Association, 106, 594–607. [Slide], [Video] 

14. [Paper Review] Gan, Lingrui, Naveen N. Narisetty, and Feng Liang. "Bayesian regularization for graphical models with unequal shrinkage." Journal of the American Statistical Association 114.527 (2019): 1218-1231. [Slide], [Video] 

1. [Paper review] Peters, Jonas, and Peter Bühlmann. "Identifiability of Gaussian structural equation models with equal error variances." Biometrika 101.1 (2014): 219-228. [Slide], [Video] 

2. [Paper review] Park, Gunwoong. "Identifiability of Additive Noise Models Using Conditional Variances" Journal of Machine Learning Research 21.75 (2020): 1-34. [Slide], [Video] 

3. [Paper review] Park, Gunwoong and Youngwhan Kim."Identifiability of Gaussian Structural Equation Models with Homogeneous and Heterogeneous Error Variances" Journal of the Korean Statistical Society 49.1 (2020): 276-292. [Slide], [Video] 

4. [Paper review] Ghoshal, Asish, and Jean Honorio. "Learning linear structural equation models in polynomial time and sample complexity." International Conference on Artificial Intelligence and Statistics. PMLR, 2018. [Slide], [Video] 

5. [Paper review] Chen, Wenyu, Mathias Drton, and Y. Samuel Wang. "On causal discovery with an equal-variance assumption." Biometrika 106.4 (2019): 973-980. [Slide], [Video] 

6. [Paper review] Park, Gunwoong, et al. "Learning a high-dimensional linear structural equation model via l1-regularized regression." Journal of Machine Learning Research 22.102 (2021): 1-41. [Slide], [Video]

7. [Paper review] Gan, Lingrui, Naveen N. Narisetty, and Feng Liang. "Bayesian regularization for graphical models with unequal shrinkage." Journal of the American Statistical Association 114.527 (2019): 1218-1231. [Slide], [Video] 

8. [Paper review] Gao, Ming, Wai Ming Tai, and Bryon Aragam. "Optimal estimation of Gaussian DAG models." International Conference on Artificial Intelligence and Statistics. PMLR, 2022. [Slide], [Video] 

9. [Paper review] How to conduct animated EDA [Slide], [Video] 

10. [Book review] Bayesian networks with example in R. [Slide]

11. [Book review] Plaat, Aske. "Deep Reinforcement Learning." arXiv preprint arXiv:2201.02135 (2022).