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Sanghack Lee
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Structural causal bandits: Where to intervene?
S Lee, E Bareinboim
Advances in neural information processing systems 31, 2018
1062018
Fairness in algorithmic decision making: An excursion through the lens of causality
A Khademi, S Lee, D Foley, V Honavar
The World Wide Web Conference, 2907-2914, 2019
1002019
General Identifiability with Arbitrary Surrogate Experiments
S Lee, JD Correa, E Bareinboim
Thirty-fifth Conference on Uncertainty in Artificial Intelligence (UAI 2019), 2019
882019
Discovery of hidden similarity on collaborative filtering to overcome sparsity problem
S Lee, J Yang, SY Park
Discovery Science: 7th International Conference, DS 2004, Padova, Italy …, 2004
772004
Structural Causal Bandits with Non-manipulable Variables
S Lee, E Bareinboim
Thirty-third Conference on Artificial Intelligence (AAAI 2019), 2019
642019
Generalized Transportability: Synthesis of Experiments from Heterogeneous Domains
S Lee, JD Correa, E Bareinboim
Thirty-fourth Conference on Artificial Intelligence (AAAI 2020), 2020
42*2020
Nested Counterfactual Identification from Arbitrary Surrogate Experiments
JD Correa, S Lee, E Bareinboim
Advances in Neural Information Processing Systems 34, 2021
402021
Transportability from multiple environments with limited experiments
E Bareinboim, S Lee, V Honavar, J Pearl
Advances in Neural Information Processing Systems 26, 2013
352013
On learning causal models from relational data
S Lee, V Honavar
Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016), 2016
312016
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe
S Lee, E Bareinboim
Advances in Neural Information Processing Systems 33, 2020
302020
Causal Effect Identifiability under Partial-Observability
S Lee, E Bareinboim
Thirty-seventh International Conference on Machine Learning, 2020
272020
m-transportability: Transportability of a causal effect from multiple environments
S Lee, V Honavar
Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2013), 2013
262013
Teens are from mars, adults are from venus: analyzing and predicting age groups with behavioral characteristics in instagram
K Han, S Lee, JY Jang, Y Jung, D Lee
Proceedings of the 8th ACM Conference on Web Science, 35-44, 2016
212016
Causal Identification with Matrix Equations
S Lee, E Bareinboim
Advances in Neural Information Processing Systems 34, 2021
152021
Towards robust relational causal discovery
S Lee, V Honavar
Thirty-fifth Conference on Uncertainty in Artificial Intelligence (UAI 2019), 2019
142019
Causal transportability of experiments on controllable subsets of variables: z-transportability
S Lee, V Honavar
Twenty-ninth Conference on Uncertainty in Artificial Intelligence (UAI 2013 …, 2013
142013
Self-discrepancy conditional independence test
S Lee, VG Honavar
Thirty-third Conference on Uncertainty in Artificial Intelligence (UAI 2017), 2017
112017
A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics.
S Lee, VG Honavar
Thirty-second Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016
112016
A kernel conditional independence test for relational data
S Lee, V Honavar
Thirty-third Conference on Uncertainty in Artificial Intelligence (UAI 2017), 2017
72017
Lifted representation of relational causal models revisited: Implications for reasoning and structure learning
S Lee, V Honavar
UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st …, 2015
72015
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학술자료 1–20