1 citations · 1 across the 6 of their papers we have counts for
7 papers
Exploration strategies for Gaussian process bandit optimization with inference regret guarantees
Hwanwoo Kim, Jungwoo Hong, Kyungjae Lee
Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output. This gap is critical under noisy e…
Implicit Q-Learning and SARSA: Liberating Policy Control from Step-Size Calibration
Hwanwoo Kim, Eric Laber
Q-learning and SARSA are foundational reinforcement learning algorithms whose practical success depends critically on step-size calibration. Step-sizes that are too large can cause…
Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber
Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD() is highly sensitive to the choice of step-size and thu…
Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design
Hwanwoo Kim, Simon Mak, Ann-Kathrin Schuetz +1
With advances in scientific computing, computer experiments are increasingly used for optimizing complex systems. However, for modern applications, e.g., the optimization of nuclea…
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
Hwanwoo Kim, Chong Liu, Yuxin Chen
Bayesian optimization (BO) is a widely used iterative algorithm for optimizing black-box functions. Each iteration requires maximizing an acquisition function, such as the upper co…
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion
Hwanwoo Kim, Panos Toulis, Eric Laber
Temporal difference (TD) learning is a foundational algorithm in reinforcement learning (RL). For nearly forty years, TD learning has served as a workhorse for applied RL as well a…
ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift
Hwanwoo Kim, Xin Zhang, Jiwei Zhao +1
The presence of distribution shifts poses a significant challenge for deploying modern machine learning models in real-world applications. This work focuses on the target shift pro…