Kim, Kyurae

13 publications

NeurIPS 2025 Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization Natalie Maus, Kyurae Kim, Yimeng Zeng, Haydn Thomas Jones, Fangping Wan, Marcelo Der Torossian Torres, Cesar de la Fuente-Nunez, Jacob R. Gardner

NeurIPS 2025 Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference Kyurae Kim, Yian Ma, Trevor Campbell, Jacob R. Gardner

AISTATS 2025 Personalized Convolutional Dictionary Learning of Physiological Time Series Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre

ICML 2025 Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell

NeurIPS 2024 Approximation-Aware Bayesian Optimization Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner

ICML 2024 Demystifying SGD with Doubly Stochastic Gradients Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner

AISTATS 2024 Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? Kyurae Kim, Yian Ma, Jacob Gardner

ICML 2024 Provably Scalable Black-Box Variational Inference with Structured Variational Families Joohwan Ko, Kyurae Kim, Woo Chang Kim, Jacob R. Gardner

AISTATS 2024 Stochastic Approximation with Biased MCMC for Expectation Maximization Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob Gardner

NeurIPS 2023 On the Convergence of Black-Box Variational Inference Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma, Jacob Gardner

ICML 2023 Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner

NeurIPS 2023 The Behavior and Convergence of Local Bayesian Optimization Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob Gardner

NeurIPS 2022 Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim, Jisu Oh, Jacob Gardner, Adji Bousso Dieng, Hongseok Kim