Recent advances in Bayesian optimization
Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
Time-varying convex optimization: Time-structured algorithms and applications
Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …
basis. Recent years have witnessed a major shift from traditional optimization paradigms …
On kernelized multi-armed bandits
We consider the stochastic bandit problem with a continuous set of arms, with the expected
reward function over the arms assumed to be fixed but unknown. We provide two new …
reward function over the arms assumed to be fixed but unknown. We provide two new …
Adversarially robust optimization with Gaussian processes
In this paper, we consider the problem of Gaussian process (GP) optimization with an added
robustness requirement: The returned point may be perturbed by an adversary, and we …
robustness requirement: The returned point may be perturbed by an adversary, and we …
Distributionally robust Bayesian optimization
Robustness to distributional shift is one of the key challenges of contemporary machine
learning. Attaining such robustness is the goal of distributionally robust optimization, which …
learning. Attaining such robustness is the goal of distributionally robust optimization, which …
Provably efficient online hyperparameter optimization with population-based bandits
Many of the recent triumphs in machine learning are dependent on well-tuned
hyperparameters. This is particularly prominent in reinforcement learning (RL) where a small …
hyperparameters. This is particularly prominent in reinforcement learning (RL) where a small …
Non-stationary bandits with auto-regressive temporal dependency
Traditional multi-armed bandit (MAB) frameworks, predominantly examined under stochastic
or adversarial settings, often overlook the temporal dynamics inherent in many real-world …
or adversarial settings, often overlook the temporal dynamics inherent in many real-world …
Bayesian generational population-based training
Reinforcement learning (RL) offers the potential for training generally capable agents that
can interact autonomously in the real world. However, one key limitation is the brittleness of …
can interact autonomously in the real world. However, one key limitation is the brittleness of …
Safe and efficient model-free adaptive control via Bayesian optimization
Adaptive control approaches yield high-performance controllers when a precise system
model or suitable parametrizations of the controller are available. Existing data-driven …
model or suitable parametrizations of the controller are available. Existing data-driven …
Corruption-tolerant gaussian process bandit optimization
We consider the problem of optimizing an unknown (typically non-convex) function with a
bounded norm in some Reproducing Kernel Hilbert Space (RKHS), based on noisy bandit …
bounded norm in some Reproducing Kernel Hilbert Space (RKHS), based on noisy bandit …