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2020 – today
- 2024
[c8]Kyurae Kim, Yi-An Ma, Jacob R. Gardner:
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS 2024: 235-243- [c7]Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner:
Stochastic Approximation with Biased MCMC for Expectation Maximization. AISTATS 2024: 2332-2340 - [c6]Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner:
Demystifying SGD with Doubly Stochastic Gradients. ICML 2024 - [c5]Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner:
Provably Scalable Black-Box Variational Inference with Structured Variational Families. ICML 2024 - [i10]Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner:
Provably Scalable Black-Box Variational Inference with Structured Variational Families. CoRR abs/2401.10989 (2024) - [i9]Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Jacob R. Gardner:
Stochastic Approximation with Biased MCMC for Expectation Maximization. CoRR abs/2402.17870 (2024) - [i8]Kyurae Kim, Joohwan Ko, Yi-An Ma, Jacob R. Gardner:
Demystifying SGD with Doubly Stochastic Gradients. CoRR abs/2406.00920 (2024) - [i7]Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner:
Approximation-Aware Bayesian Optimization. CoRR abs/2406.04308 (2024) - 2023
- [c4]Kyurae Kim
, Kaiwen Wu, Jisu Oh, Jacob R. Gardner:
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. ICML 2023: 16853-16876 - [c3]Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner:
On the Convergence of Black-Box Variational Inference. NeurIPS 2023 - [c2]Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner:
The Behavior and Convergence of Local Bayesian Optimization. NeurIPS 2023 - [i6]Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner:
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. CoRR abs/2303.10472 (2023) - [i5]Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner:
Black-Box Variational Inference Converges. CoRR abs/2305.15349 (2023) - [i4]Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner:
The Behavior and Convergence of Local Bayesian Optimization. CoRR abs/2305.15572 (2023) - [i3]Kyurae Kim, Yi-An Ma, Jacob R. Gardner:
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? CoRR abs/2307.14642 (2023) - 2022
- [c1]Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim:
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. NeurIPS 2022 - [i2]Kyurae Kim, Youngjae Kim, Sungyong Park:
A Probabilistic Machine Learning Approach to Scheduling Parallel Loops with Bayesian Optimization. CoRR abs/2206.05787 (2022) - [i1]Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim:
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. CoRR abs/2206.06295 (2022)
Coauthor Index
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