ICML 2026, 2026
Introduces PASV, a Shapley-based attribution framework that incorporates precedence constraints and contributor-specific priority weights for more structure-faithful data valuation and feature attribution.
Recommended citation: Lee, K., Liu, Z., Tang, W., and Zhang, Y. (2026). "Priority-Aware Shapley Value." International Conference on Machine Learning (ICML).
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NeurIPS 2025, 2025
Develops a robust and efficient method for group-level data valuation that is resistant to strategic group splitting and supported by theoretical guarantees.
Recommended citation: Lee, K., Liu, Z., Tang, W., and Zhang, Y. (2025). "Faithful Group Shapley Value." Advances in Neural Information Processing Systems (NeurIPS). ICML 2025 DataWorld Workshop Best Paper Award Honorable Mention.
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ICLR 2025, 2025
A computationally efficient conformal prediction method that leverages leave-one-out stability to improve uncertainty quantification for black-box models.
Recommended citation: Lee, K. and Zhang, Y. (2025). "Leave-One-Out Stable Conformal Prediction." The Thirteenth International Conference on Learning Representations (ICLR).
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ICML 2026 CTB Workshop, 2026
Extends priority-aware valuation to arbitrary directed weighted priority graphs, enabling data and model valuation when pairwise preferences are cyclic, soft, or multi-criterion.
Recommended citation: Lee, K., Liu, Z., Tang, W., and Zhang, Y. (2026). "Generalized Priority-Aware Shapley Value." ICML 2026 CTB Workshop.
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ICML 2026 NExT-Game Workshop, 2026
A statistical efficiency framework for estimating probabilistic values, including Shapley values and semivalues, with a new estimator guided by first-order mean squared error.
Recommended citation: Liu, Z., Lee, K., Zhang, Y., and Tang, W. (2026). "First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint." ICML 2026 NExT-Game Workshop.
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Journal of Applied Statistics, 2025
A Bayesian hierarchical model for studying time-varying nonlinear lagged associations, applied to temperature and COVID-19 confirmed cases.
Recommended citation: Han, D., Lee, K., Chung, Y., Kobayashi, G., and Choi, T. (2025). "Bayesian Hierarchical Time-Varying Distributed Lag Nonlinear Model: Applications in the Short-Term Association between Ambient Temperature and Daily Confirmed Cases of COVID-19." Journal of Applied Statistics.