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

A Probabilistic Machine Learning Approach to Scheduling Parallel Loops with Bayesian Optimization. - Kyurae Kim; Youngjae Kim 001; Sungyong Park (CoRR, 2022)

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

On the Convergence of Black-Box Variational Inference. - Kyurae Kim; Jisu Oh; Kaiwen Wu; Yi-An Ma; Jacob R. Gardner (NeurIPS, 2023)

The Behavior and Convergence of Local Bayesian Optimization. - Kaiwen Wu; Kyurae Kim; Roman Garnett; Jacob R. Gardner (NeurIPS, 2023)

Black-Box Variational Inference Converges. - Kyurae Kim; Kaiwen Wu; Jisu Oh; Yi-An Ma; Jacob R. Gardner (CoRR, 2023)

Stochastic Approximation with Biased MCMC for Expectation Maximization. - Samuel Gruffaz; Kyurae Kim; Alain Durmus; Jacob R. Gardner (AISTATS, 2024)

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? - Kyurae Kim; Yi-An Ma; Jacob R. Gardner (AISTATS, 2024)

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

Provably Scalable Black-Box Variational Inference with Structured Variational Families. - Joohwan Ko; Kyurae Kim; Woochang Kim; Jacob R. Gardner (ICML, 2024)

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

Personalized Convolutional Dictionary Learning of Physiological Time Series. - Axel Roques; Samuel Gruffaz; Kyurae Kim; Alain Oliviero Durmus; Laurent Oudre (AISTATS, 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 (CoRR, 2025)

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. - Kyurae Kim; Zuheng Xu; Jacob R. Gardner; Trevor Campbell (CoRR, 2025)

Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference. - Kyurae Kim; Yi-An Ma; Trevor Campbell; Jacob R. Gardner (CoRR, 2025)