Sanghack Lee

According to our database1, Sanghack Lee authored at least 31 papers between 2004 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction.
CoRR, 2024

Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

On Positivity Condition for Causal Inference.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
CoRR, 2023

On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022
Counterfactual Transportability: A Formal Approach.
Proceedings of the International Conference on Machine Learning, 2022

SNU-Causality Lab @ Causal News Corpus 2022: Detecting Causality by Data Augmentation via Part-of-Speech tagging.
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, 2022

2021
Causal Identification with Matrix Equations.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Nested Counterfactual Identification from Arbitrary Surrogate Experiments.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Effect Identifiability under Partial-Observability.
Proceedings of the 37th International Conference on Machine Learning, 2020

General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Identifiability from a Combination of Observations and Experiments.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality.
Proceedings of the World Wide Web Conference, 2019

Towards Robust Relational Causal Discovery.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

General Identifiability with Arbitrary Surrogate Experiments.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Structural Causal Bandits with Non-Manipulable Variables.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Structural Causal Bandits: Where to Intervene?
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Towards Conditional Independence Test for Relational Data.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Self-Discrepancy Conditional Independence Test.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

2016
Teens are from mars, adults are from venus: analyzing and predicting age groups with behavioral characteristics in instagram.
Proceedings of the 8th ACM Conference on Web Science, 2016

A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

On Learning Causal Models from Relational Data.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning.
Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), 2015

2013
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Transportability from Multiple Environments with Limited Experiments.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Learning Classifiers from Distributional Data.
Proceedings of the IEEE International Congress on Big Data, 2013

m-Transportability: Transportability of a Causal Effect from Multiple Environments.
Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

2006
A New Polynomial Time Algorithm for Bayesian Network Structure Learning.
Proceedings of the Advanced Data Mining and Applications, Second International Conference, 2006

2004
Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem.
Proceedings of the Discovery Science, 7th International Conference, 2004


  Loading...