Chung, H., Kim, J., Jo, H., & Choi, H. (2024). Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity.
Jun, K.S., & Kim, J. (2024). Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization.
Kim, J. (2024). Beyond Regrets: Geometric Metrics for Bayesian Optimization.
Kim, J., Li, M., Li, Y., Gomez, A., Hinder, O., & Leu, P.W. (2024). Multi-BOWS: multi-fidelity multi-objective Bayesian optimization with warm starts for nanophotonic structure design. DIGITAL DISCOVERY, 3(2), 381-391.Royal Society of Chemistry (RSC). doi: 10.1039/d3dd00177f.
Martin, K., Shanks, K., Liu, Y., Kim, J., Haghanifar, S., Zarei, M., Sharma, S., & Leu, P.W. (2024). Minimizing annual reflection loss in fixed-tilt photovoltaic modules using graded refractive index (GRIN) anti-reflective glass. SOLAR ENERGY, 272, 112424.Elsevier. doi: 10.1016/j.solener.2024.112424.
Zarei, M., Li, M., Medvedeva, E.E., Sharma, S., Kim, J., Shao, Z., Walker, S.B., LeMieux, M., Liu, Q., & Leu, P.W. (2024). Flexible Embedded Metal Meshes by Sputter-Free Crack Lithography for Transparent Electrodes and Electromagnetic Interference Shielding. ACS Appl Mater Interfaces, 16(5), 6382-6393.American Chemical Society (ACS). doi: 10.1021/acsami.3c16405.
Kim, J. (2023). Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning.
Kim, J., & Choi, S. (2023). BayesO: A Bayesian optimization framework in Python. The Journal of Open Source Software, 8(90), 5320.The Open Journal. doi: 10.21105/joss.05320.
Kim, J., Li, M., Hinder, O., & Leu, P.W. (2023). Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations.
Kim, J., Yoon, J., & Cho, M. (2023). Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions.
Ahn, S., Kim, J., Cho, M., & Park, J. (2022). Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction.
Kim, J., & Choi, S. (2022). On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization.
Lee, J., Kim, J., Chung, H., Park, J., & Cho, M. (2022). Learning to Assemble Geometric Shapes.
Thompson, R., Knyazev, B., Ghalebi, E., Kim, J., & Taylor, G.W. (2022). On Evaluation Metrics for Graph Generative Models.
Chung, H., Kim, J., Knyazev, B., Lee, J., Taylor, G.W., Park, J., & Cho, M. (2021). Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning.
Kim, J., McCourt, M., You, T., Kim, S., & Choi, S. (2021). Bayesian optimization with approximate set kernels. MACHINE LEARNING, 110(5), 857-879.Springer Nature. doi: 10.1007/s10994-021-05949-0.
Kim, J., Choi, S., & Cho, M. (2020). Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytopes.
Kim, J., Chung, H., Lee, J., Cho, M., & Park, J. (2020). Combinatorial 3D Shape Generation via Sequential Assembly.
Lee, J., Lee, Y., Kim, J., Yang, E., Hwang, S.J., & Teh, Y.W. (2020). Bootstrapping Neural Processes.
Kim, J., & Choi, S. (2019). On Local Optimizers of Acquisition Functions in Bayesian Optimization.
Kim, J., & Choi, S. (2019). Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization.
Park, M., Kim, J., Kim, S., Liu, Y., & Choi, S. (2019). MxML: Mixture of Meta-Learners for Few-Shot Classification.
Lee, J., Lee, Y., Kim, J., Kosiorek, A.R., Choi, S., & Teh, Y.W. (2018). Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks.
Kim, J., Kim, S., & Choi, S. (2017). Learning to Warm-Start Bayesian Hyperparameter Optimization.
Chung, H., Kim, J., Jo, H., & Choi, H. (2024). Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, (pp. 3704-3708).Association for Computing Machinery (ACM). doi: 10.1145/3627673.3679920.