29 citations · 72 across the 9 of their papers we have counts for
9 papers
The Behavior and Convergence of Local Bayesian Optimization
Kaiwen Wu, Kyurae Kim, Roman Garnett +1
A recent development in Bayesian optimization is the use of local optimization strategies, which can deliver strong empirical performance on high-dimensional problems compared to t…
On the Convergence of Black-Box Variational Inference
Kyurae Kim, Jisu Oh, Kaiwen Wu +2
We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference. While preliminary investigations worked…
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Kyurae Kim, Kaiwen Wu, Jisu Oh +1
Understanding the gradient variance of black-box variational inference (BBVI) is a crucial step for establishing its convergence and developing algorithmic improvements. However, e…
Local Bayesian optimization via maximizing probability of descent
Quan Nguyen, Kaiwen Wu, Jacob R. Gardner +1
Local optimization presents a promising approach to expensive, high-dimensional black-box optimization by sidestepping the need to globally explore the search space. For objective…
Discovering Many Diverse Solutions with Bayesian Optimization
Natalie Maus, Kaiwen Wu, David Eriksson +1
Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions. While BO has been successfully applied to a wide range of scien…
Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu, Allen Houze Wang, Yaoliang Yu
Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to "small, imperceptible" perturbations known as adversarial attacks. While the majority of ex…
Newton-type Methods for Minimax Optimization
Guojun Zhang, Kaiwen Wu, Pascal Poupart +1
Differential games, in particular two-player sequential zero-sum games (a.k.a. minimax optimization), have been an important modeling tool in applied science and received renewed i…
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin
Kaiwen Wu, Yaoliang Yu
Deep models, while being extremely versatile and accurate, are vulnerable to adversarial attacks: slight perturbations that are imperceptible to humans can completely flip the pred…
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao +3
In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient…