| 2025 | AISTATS | Personalized Convolutional Dictionary Learning of Physiological Time Series. | Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre |
| 2025 | ICML | Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. | Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell |
| 2024 | AISTATS | Stochastic Approximation with Biased MCMC for Expectation Maximization. | Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner |
| 2024 | AISTATS | Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? | Kyurae Kim, Yi-An Ma, Jacob R. Gardner |
| 2024 | ICML | Demystifying SGD with Doubly Stochastic Gradients. | Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner |
| 2024 | ICML | Provably Scalable Black-Box Variational Inference with Structured Variational Families. | Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner |
| 2023 | ICML | Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. | Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner |