Kyurae Kim

According to our database, Kyurae Kim authored at least 15 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference.

Kyurae Kim, Yi-An Ma, Trevor Campbell, Jacob R. Gardner

CoRR, May, 2025

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization.

Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell

CoRR, March, 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, January, 2025

Personalized Convolutional Dictionary Learning of Physiological Time Series.

Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Approximation-Aware Bayesian Optimization.

Natalie Maus, Kyurae Kim, David Eriksson, Geoff Pleiss, John P. Cunningham, Jacob R. Gardner

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Provably Scalable Black-Box Variational Inference with Structured Variational Families.

Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Demystifying SGD with Doubly Stochastic Gradients.

Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?

Kyurae Kim, Yi-An Ma, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Stochastic Approximation with Biased MCMC for Expectation Maximization.

Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Black-Box Variational Inference Converges.

Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner

CoRR, 2023

The Behavior and Convergence of Local Bayesian Optimization.

Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Convergence of Black-Box Variational Inference.

Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference.

Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner

Proceedings of the International Conference on Machine Learning, 2023

2022

A Probabilistic Machine Learning Approach to Scheduling Parallel Loops with Bayesian Optimization.

Kyurae Kim, Youngjae Kim, Sungyong Park

CoRR, 2022

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022