Yonghan Jung

According to our database, Yonghan Jung authored at least 17 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Data-Driven Information-Theoretic Causal Bounds under Unmeasured Confounding.

Yonghan Jung, Bogyeong Kang

CoRR, January, 2026

Benchmarking Visual Feature Representations for LiDAR-Inertial-Visual Odometry Under Challenging Conditions.

Eunseon Choi, Junwoo Hong, Daehan Lee, Sanghyun Park, Hyunyoung Jo, Sunyoung Kim, Changho Kang, Seongsam Kim, Yonghan Jung, Jungwook Park, Seul Koo, Soohee Han

IEEE Access, 2026

2025

Debiased Front-Door Learners for Heterogeneous Effects.

Yonghan Jung

CoRR, September, 2025

Path-specific effects for pulse-oximetry guided decisions in critical care.

Kevin Zhang, Yonghan Jung, Divyat Mahajan, Karthikeyan Shanmugam, Shalmali Joshi

CoRR, June, 2025

Sufficient Invariant Learning for Distribution Shift.

Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference.

Yonghan Jung, Min Woo Park, Sanghack Lee

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Efficient Policy Evaluation Across Multiple Different Experimental Datasets.

Yonghan Jung, Alexis Bellot

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Unified Covariate Adjustment for Causal Inference.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Estimating Causal Effects Identifiable from a Combination of Observations and Experiments.

Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Estimating Joint Treatment Effects by Combining Multiple Experiments.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2023

2022

On Measuring Causal Contributions via do-interventions.

Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2022

2021

Double Machine Learning Density Estimation for Local Treatment Effects with Instruments.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the 38th International Conference on Machine Learning, 2021

Estimating Identifiable Causal Effects through Double Machine Learning.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Learning Causal Effects via Weighted Empirical Risk Minimization.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Estimating Causal Effects Using Weighting-Based Estimators.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2017

Detection of PVC by using a wavelet-based statistical ECG monitoring procedure.

Yonghan Jung, Heeyoung Kim

Biomed. Signal Process. Control., 2017