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2020 – today
- 2024
[c29]Soheun Yi, Sanghack Lee:
Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models. AISTATS 2024: 1135-1143- [c28]Inwoo Hwang, Yesong Choe, Yeahoon Kwon, Sanghack Lee:
On Positivity Condition for Causal Inference. ICML 2024 - [c27]Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, Sanghack Lee:
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning. ICML 2024 - [i9]Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee:
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition. CoRR abs/2405.07220 (2024) - [i8]Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-Tak Zhang:
Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction. CoRR abs/2406.00614 (2024) - [i7]Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, Sanghack Lee:
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning. CoRR abs/2406.03234 (2024) - 2023
- [c26]Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee:
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition. CLeaR 2023: 448-472 - [i6]Chanhui Lee, Juhyeon Kim, Yongjun Jeong, Juhyun Lyu, Junghee Kim, Sangmin Lee, Sangjun Han, Hyeokjun Choe, Soyeon Park, Woohyung Lim, Sungbin Lim, Sanghack Lee:
Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms? CoRR abs/2311.11212 (2023) - 2022
- [c25]Juhyeon Kim, Yesong Choe, Sanghack Lee:
SNU-Causality Lab @ Causal News Corpus 2022: Detecting Causality by Data Augmentation via Part-of-Speech tagging. CASE@EMNLP 2022: 44-49 - [c24]Juan D. Correa, Sanghack Lee, Elias Bareinboim:
Counterfactual Transportability: A Formal Approach. ICML 2022: 4370-4390 - 2021
- [c23]Juan D. Correa, Sanghack Lee, Elias Bareinboim:
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. NeurIPS 2021: 6856-6867 - [c22]Sanghack Lee, Elias Bareinboim:
Causal Identification with Matrix Equations. NeurIPS 2021: 9468-9479 - [i5]Juan D. Correa, Sanghack Lee, Elias Bareinboim:
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. CoRR abs/2107.03190 (2021) - 2020
- [c21]Sanghack Lee, Juan D. Correa, Elias Bareinboim:
General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains. AAAI 2020: 10210-10217 - [c20]Sanghack Lee, Juan D. Correa, Elias Bareinboim:
Identifiability from a Combination of Observations and Experiments. AAAI 2020: 13677-13680 - [c19]Sanghack Lee, Elias Bareinboim:
Causal Effect Identifiability under Partial-Observability. ICML 2020: 5692-5701 - [c18]Sanghack Lee, Elias Bareinboim:
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe. NeurIPS 2020
2010 – 2019
- 2019
- [c17]Sanghack Lee, Elias Bareinboim:
Structural Causal Bandits with Non-Manipulable Variables. AAAI 2019: 4164-4172 - [c16]Sanghack Lee, Vasant G. Honavar:
Towards Robust Relational Causal Discovery. UAI 2019: 345-355 - [c15]Sanghack Lee, Juan D. Correa, Elias Bareinboim:
General Identifiability with Arbitrary Surrogate Experiments. UAI 2019: 389-398 - [c14]Aria Khademi, Sanghack Lee, David Foley, Vasant G. Honavar
:
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. WWW 2019: 2907-2914 - [i4]Aria Khademi, Sanghack Lee, David Foley, Vasant G. Honavar:
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. CoRR abs/1903.11719 (2019) - [i3]Sanghack Lee, Vasant G. Honavar:
Towards Robust Relational Causal Discovery. CoRR abs/1912.02390 (2019) - 2018
- [c13]Sanghack Lee, Elias Bareinboim:
Structural Causal Bandits: Where to Intervene? NeurIPS 2018: 2573-2583 - 2017
- [c12]Sanghack Lee, Vasant G. Honavar:
Self-Discrepancy Conditional Independence Test. UAI 2017 - [c11]Sanghack Lee, Vasant G. Honavar:
Towards Conditional Independence Test for Relational Data. UAI 2017 - 2016
- [c10]Sanghack Lee, Vasant G. Honavar:
On Learning Causal Models from Relational Data. AAAI 2016: 3263-3270 - [c9]Sanghack Lee, Vasant G. Honavar:
A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics. UAI 2016 - [c8]Kyungsik Han, Sanghack Lee, Jin Yea Jang, Yong Jung, Dongwon Lee:
Teens are from mars, adults are from venus: analyzing and predicting age groups with behavioral characteristics in instagram. WebSci 2016: 35-44 - 2015
- [c7]Sanghack Lee, Vasant G. Honavar:
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning. ACI@UAI 2015: 56-65 - [i2]Sanghack Lee, Vasant G. Honavar:
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning. CoRR abs/1508.02103 (2015) - 2013
- [c6]Sanghack Lee, Vasant G. Honavar:
m-Transportability: Transportability of a Causal Effect from Multiple Environments. AAAI 2013: 583-590 - [c5]Harris T. Lin, Sanghack Lee, Ngot Bui, Vasant G. Honavar:
Learning Classifiers from Distributional Data. BigData Congress 2013: 302-309 - [c4]Elias Bareinboim, Sanghack Lee, Vasant G. Honavar, Judea Pearl:
Transportability from Multiple Environments with Limited Experiments. NIPS 2013: 136-144 - [c3]Sanghack Lee, Vasant G. Honavar:
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. UAI 2013 - [i1]Sanghack Lee, Vasant G. Honavar:
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. CoRR abs/1309.6842 (2013)
2000 – 2009
- 2006
- [c2]Sanghack Lee, Jihoon Yang, Sungyong Park:
A New Polynomial Time Algorithm for Bayesian Network Structure Learning. ADMA 2006: 501-508 - 2004
- [c1]Sanghack Lee, Jihoon Yang, Sung-Yong Park:
Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem. Discovery Science 2004: 396-402
Coauthor Index
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