I have published papers in top AI and ML conferences (UAI, NIPS, AAAI) as first author.

* equally contributed, first author, co-first, , second

2019

  • Sanghack Lee, Juan D. Correa, and Elias Bareinboim, General Identifiability with Arbitrary Surrogate Experiments In Proceedings of the Thirty-fifth Conference on Uncertainty in Artificial Intelligence (UAI 2019) (Acceptance rate 26% (118/450))

  • Sanghack Lee and Vasant Honavar, Towards Robust Relational Causal Discovery In Proceedings of the Thirty-fifth Conference on Uncertainty in Artificial Intelligence (UAI 2019) (Acceptance rate 26% (118/450))

  • Aria Khademi, Sanghack Lee, David Foley, and Vasant Honavar, Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality In Proceedings of The Web Conference 2019 (WWW 2019) (forthcoming)

  • Sanghack Lee and Elias Bareinboim, On Structural Causal Bandit with Non-manipulable Variables In Proceedings of Thirty-third Conference on Artificial Intelligence (AAAI 2019) (Acceptance rate 16.2% (1150/7095)) [paper], [poster], [slides]

2018

  • Sanghack Lee and Elias Bareinboim, Structural Causal Bandits: Where to Intervene? In Advances in Neural Information Processing Systems 32nd. To appear (Acceptance rate 20.8% (1011/4856)) [paper], [code], [poster]

2017

  • Sanghack Lee and Vasant Honavar, A Kernel Conditional Independence Test for Relational Data. In Proceedings of the Thirty-third Conference on Uncertainty in Artificial Intelligence (UAI 2017). (Acceptance rate 31% (87/282)) [code], [paper]
  • Sanghack Lee and Vasant Honavar, Self-Discrepancy Conditional Independence Test. In Proceedings of the Thirty-third Conference on Uncertainty in Artificial Intelligence (UAI 2017). (Acceptance rate 31% (87/282)) [code], [paper]

2016

  • Sanghack Lee and Vasant Honavar, A Characterization of Markov Equivalence Classes for Relational Causal Model with Path Semantics. In Proceedings of the Thirty-second Conference on Uncertainty in Artificial Intelligence (UAI 2016). (Acceptance rate 31% (85/275)) [code], [paper], [appendix]
  • Kyungsik Han, Sanghack Lee, Jin Yea Jang, Yong Jung, and Dongwon Lee, “Teens are from Mars, Adults are from Venus”: Analyzing and Predicting Age Groups with Behavioral Characteristics in Instagram ACM Web Science 2016 Conference (WebSci’16) [paper] (Acceptance rate 24% (17/70))
  • Sanghack Lee and Vasant Honavar, On Learning Causal Models from Relational Data. In Proceedings of Thirtieth Conference on Artificial Intelligence (AAAI 2016) (Acceptance rate 26% (549/2132)) [code] [paper]

2015

  • Sanghack Lee and Vasant Honavar, Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning In Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015). pp. 56-65 [paper]

2013

  • Elias Bareinboim*, Sanghack Lee*, Vasant Honavar, and Judea Pearl, Transportability from Multiple Environments with Limited Experiments. Advances in Neural Information Processing Systems 26. pp. 136-144 (Acceptance rate 25% (360/1420)) [paper]
  • Sanghack Lee and Vasant Honavar, Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability In Proceedings of the Twenty-ninth Conference on Uncertainty in Artificial Intelligence (UAI 2013). pp. 361-370 (Acceptance rate 31% (73/233)) [paper]
  • Sanghack Lee and Vasant Honavar, m-Transportability: Transportability of a Causal Effect from Multiple Environments In Proceedings of the Twenty-seventh Conference on Artificial Intelligence (AAAI 2013). pp. 583-590
  • Harris Lin*, Sanghack Lee*, Ngot Bui*, and Vasant Honavar, Learning Classifiers from Distributional Data In IEEE Second International Congress on Big Data. pp. 302-309

Pre-Ph.D.

  • (pre-PhD) A New Polynimial Time Algorithm for Bayesian Network Structure Learning In Proceedings of the Second International Conference on Advanced Data Mining and Applications (ADMA’06). Xi’an, China. pp. 501-508. (LNAI 4093)
  • (pre-PhD) Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem In Proceedings of the Seventh International Conference on Discovery Science (DS’04). Padova, Italy. pp. 396-402. (LNAI 3245)