Sanghack Lee

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 27

Top Topics

Conditional Independence

Gaussian Graphical Models

Top Venues

Publications

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 (2024)
Soheun Yi, Sanghack Lee
Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models. AISTATS (2024)
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 (2024)
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 (2024)
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)
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 (2023)
Juan D. Correa, Sanghack Lee, Elias Bareinboim
Counterfactual Transportability: A Formal Approach. ICML (2022)
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)
Juan D. Correa, Sanghack Lee, Elias Bareinboim
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. NeurIPS (2021)
Juan D. Correa, Sanghack Lee, Elias Bareinboim
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. CoRR (2021)
Sanghack Lee, Elias Bareinboim
Causal Identification with Matrix Equations. NeurIPS (2021)
Sanghack Lee, Juan D. Correa, Elias Bareinboim
General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains. AAAI (2020)
Sanghack Lee, Elias Bareinboim
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe. NeurIPS (2020)
Sanghack Lee, Juan D. Correa, Elias Bareinboim
Identifiability from a Combination of Observations and Experiments. AAAI (2020)
Sanghack Lee, Elias Bareinboim
Causal Effect Identifiability under Partial-Observability. ICML (2020)
Sanghack Lee, Vasant G. Honavar
Towards Robust Relational Causal Discovery. UAI (2019)
Sanghack Lee, Juan D. Correa, Elias Bareinboim
General Identifiability with Arbitrary Surrogate Experiments. UAI (2019)
Sanghack Lee, Elias Bareinboim
Structural Causal Bandits with Non-Manipulable Variables. AAAI (2019)
Aria Khademi, Sanghack Lee, David Foley, Vasant G. Honavar
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. WWW (2019)
Sanghack Lee, Vasant G. Honavar
Towards Robust Relational Causal Discovery. CoRR (2019)
Aria Khademi, Sanghack Lee, David Foley, Vasant G. Honavar
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. CoRR (2019)
Sanghack Lee, Elias Bareinboim
Structural Causal Bandits: Where to Intervene? NeurIPS (2018)
Sanghack Lee, Vasant G. Honavar
Towards Conditional Independence Test for Relational Data. UAI (2017)
Sanghack Lee, Vasant G. Honavar
Self-Discrepancy Conditional Independence Test. UAI (2017)
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)
Sanghack Lee, Vasant G. Honavar
A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics. UAI (2016)
Sanghack Lee, Vasant G. Honavar
On Learning Causal Models from Relational Data. AAAI (2016)
Sanghack Lee, Vasant G. Honavar
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning. ACI@UAI (2015)
Sanghack Lee, Vasant G. Honavar
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning. CoRR (2015)
Sanghack Lee, Vasant G. Honavar
m-Transportability: Transportability of a Causal Effect from Multiple Environments. AAAI (2013)
Harris T. Lin, Sanghack Lee, Ngot Bui, Vasant G. Honavar
Learning Classifiers from Distributional Data. BigData Congress (2013)
Sanghack Lee, Vasant G. Honavar
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. UAI (2013)
Elias Bareinboim, Sanghack Lee, Vasant G. Honavar, Judea Pearl
Transportability from Multiple Environments with Limited Experiments. NIPS (2013)
Sanghack Lee, Vasant G. Honavar
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. CoRR (2013)
Sanghack Lee, Jihoon Yang, Sungyong Park
A New Polynomial Time Algorithm for Bayesian Network Structure Learning. ADMA (2006)
Sanghack Lee, Jihoon Yang, Sung-Yong Park
Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem. Discovery Science (2004)