Search Results for author:
Found 28 papers, 16 papers with code
Vanilla Bayesian Optimization Performs Great in High Dimensions
High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization algorithms.
A General Framework for User-Guided Bayesian Optimization
The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines.
High-dimensional Bayesian Optimization with Group Testing
We propose a group testing approach to identify active variables to facilitate efficient optimization in these domains.
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces.
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance.
Out-of-Distribution Detection for Adaptive Computer Vision
It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions.
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics.
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Gaussian processes are the model of choice in Bayesian optimization and active learning.
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs.
Falsification of Cyber-Physical Systems using Bayesian Optimization
For some of the benchmark problems, the choice of acquisition function clearly affects the number of simulations needed for successful falsification.
Joint Entropy Search for Maximally-Informed Bayesian Optimization
As a light-weight approach with superior results, JES provides a new go-to acquisition function for Bayesian optimization.
$π$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration
We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems.
Bayesian Optimization
Multi-Objective Reinforcement Learning
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters.
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
Learning of Parameters in Behavior Trees for Movement Skills
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error.
Prior-guided Bayesian Optimization
While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts.
Bayesian Optimization with a Prior for the Optimum
We show that BOPrO is around 6. 67x faster than state-of-the-art methods on a common suite of benchmarks, and achieves a new state-of-the-art performance on a real-world hardware design application.
Polystore++: Accelerated Polystore System for Heterogeneous Workloads
Modern real-time business analytic consist of heterogeneous workloads (e. g, database queries, graph processing, and machine learning).
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning
In this paper, we introduce a new serial crystallography dataset comprised of real and synthetic images; the synthetic images are generated through the use of a simulator that is both scalable and accurate.
Practical Design Space Exploration
The proposed methodology follows a white-box model which is simple to understand and interpret (unlike, for example, neural networks) and can be used by the user to better understand the results of the automatic search.
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns
In this paper, we introduce a new serial crystallography dataset generated through the use of a simulator; the synthetic images are labeled and they are both scalable and accurate.
Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality
Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge.
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
In this work, we analyze the entries from DAWNBench, which received optimized submissions from multiple industrial groups, to investigate the behavior of TTA as a metric as well as trends in the best-performing entries.
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper
In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future.
Comparative Design Space Exploration of Dense and Semi-Dense SLAM
SLAM has matured significantly over the past few years, and is beginning to appear in serious commercial products.
Reasoning in complex environments with the SelectScript declarative language
SelectScript is an extendable, adaptable, and declarative domain-specific language aimed at information retrieval from simulation environments and robotic world models in an SQL-like manner.
Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging.
