Kyurae Kim

29 papers A* 8A 3Journal 18
YearRankTypeTitle / Venue / Authors
2026 J jnl
A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets.
CoRR
Samuel Gruffaz, Kyurae Kim, Fares Guehtar, Hadrien Duval-Decaix, Pacôme Trautmann
2026 J jnl
Purely Agentic Black-Box Optimization for Biological Design.
CoRR
Natalie Maus, Yimeng Zeng, Haydn Thomas Jones, Yining Huang, Gaurav Ng Goel, Alden Rose, Kyurae Kim, Hyun-Su Lee, Marcelo Der Torossian Torres, Fangping Wan, Cesar de la Fuente-Nunez, Mark Yatskar, Osbert Bastani, Jacob R. Gardner
2026 J jnl
Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space.
CoRR
Kyurae Kim, Qiang Fu, Yi-An Ma, Jacob R. Gardner, Trevor Campbell
2025 J jnl
Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped.
CoRR
Kyurae Kim, Samuel Gruffaz, Ji Won Park, Alain Oliviero Durmus
2025 J jnl
Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization.
CoRR
Natalie Maus, Kyurae Kim, Yimeng Zeng, Haydn Thomas Jones, Fangping Wan, Marcelo Der Torossian Torres, Cesar de la Fuente-Nunez, Jacob R. Gardner
2025 J jnl
Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference.
CoRR
Kyurae Kim, Yi-An Ma, Trevor Campbell, Jacob R. Gardner
2025 A conf
Personalized Convolutional Dictionary Learning of Physiological Time Series.
AISTATS
Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre
2025 J jnl
Personalized Convolutional Dictionary Learning of Physiological Time Series.
CoRR
Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre
2025 A* conf
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization.
ICML
Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell
2025 J jnl
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization.
CoRR
Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell
2024 A* conf
Approximation-Aware Bayesian Optimization.
NeurIPS
Natalie Maus, Kyurae Kim, David Eriksson, Geoff Pleiss, John P. Cunningham, Jacob R. Gardner
2024 J jnl
Approximation-Aware Bayesian Optimization.
CoRR
Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
2024 A* conf
Demystifying SGD with Doubly Stochastic Gradients.
ICML
Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner
2024 J jnl
Demystifying SGD with Doubly Stochastic Gradients.
CoRR
Kyurae Kim, Joohwan Ko, Yi-An Ma, Jacob R. Gardner
2024 A conf
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
AISTATS
Kyurae Kim, Yi-An Ma, Jacob R. Gardner
2024 A* conf
Provably Scalable Black-Box Variational Inference with Structured Variational Families.
ICML
Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner
2024 J jnl
Provably Scalable Black-Box Variational Inference with Structured Variational Families.
CoRR
Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner
2024 A conf
Stochastic Approximation with Biased MCMC for Expectation Maximization.
AISTATS
Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner
2024 J jnl
Stochastic Approximation with Biased MCMC for Expectation Maximization.
CoRR
Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Jacob R. Gardner
2023 J jnl
Black-Box Variational Inference Converges.
CoRR
Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner
2023 J jnl
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
CoRR
Kyurae Kim, Yi-An Ma, Jacob R. Gardner
2023 A* conf
On the Convergence of Black-Box Variational Inference.
NeurIPS
Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner
2023 A* conf
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference.
ICML
Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner
2023 J jnl
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference.
CoRR
Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner
2023 A* conf
The Behavior and Convergence of Local Bayesian Optimization.
NeurIPS
Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner
2023 J jnl
The Behavior and Convergence of Local Bayesian Optimization.
CoRR
Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner
2022 J jnl
A Probabilistic Machine Learning Approach to Scheduling Parallel Loops with Bayesian Optimization.
CoRR
Kyurae Kim, Youngjae Kim, Sungyong Park
2022 A* conf
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients.
NeurIPS
Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim
2022 J jnl
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients.
CoRR
Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim