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 …
Reinforcement learning for intelligent healthcare applications: A survey
Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …
Supervised pretraining can learn in-context reinforcement learning
Large transformer models trained on diverse datasets have shown a remarkable ability to
learn in-context, achieving high few-shot performance on tasks they were not explicitly …
learn in-context, achieving high few-shot performance on tasks they were not explicitly …
Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models
Deep Learning (DL) systems have received exponential growth in popularity and have
become ubiquitous in our everyday life. Such systems are built on top of popular DL …
become ubiquitous in our everyday life. Such systems are built on top of popular DL …
[图书][B] Algorithms for decision making
A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …
underlying mathematical problem formulations and the algorithms for solving them …
[图书][B] Control systems and reinforcement learning
S Meyn - 2022 - books.google.com
A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …
Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
Scalable global optimization via local Bayesian optimization
Bayesian optimization has recently emerged as a popular method for the sample-efficient
optimization of expensive black-box functions. However, the application to high-dimensional …
optimization of expensive black-box functions. However, the application to high-dimensional …
Neural thompson sampling
Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-
armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson …
armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson …