113 citations · 294 across the 24 of their papers we have counts for
27 papers
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions w…
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Neeratyoy Mallik, Edward Bergman, Carl Hvarfner +5
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been develo…
Out-of-Distribution Detection for Adaptive Computer Vision
Simon Kristoffersson Lind, Rudolph Triebel, Luigi Nardi +1
It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a…
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
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 applicati…
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Carl Hvarfner, Erik Hellsten, Frank Hutter +1
Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full poten…
Combining Planning, Reasoning and Reinforcement Learning to solve Industrial Robot Tasks
Matthias Mayr, Faseeh Ahmad, Konstantinos Chatzilygeroudis +2
One of today's goals for industrial robot systems is to allow fast and easy provisioning for new tasks. Skill-based systems that use planning and knowledge representation have long…
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
Erik Hellsten, Artur Souza, Johannes Lenfers +7
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility ne…
Falsification of Cyber-Physical Systems using Bayesian Optimization
Zahra Ramezani, Kenan Šehić, Luigi Nardi +1
Cyber-physical systems (CPSs) are often complex and safety-critical, making it both challenging and crucial to ensure that the system's specifications are met. Simulation-based fal…
Learning Skill-based Industrial Robot Tasks with User Priors
Matthias Mayr, Carl Hvarfner, Konstantinos Chatzilygeroudis +2
Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill paramet…
Homunculus: Auto-Generating Efficient Data-Plane ML Pipelines for Datacenter Networks
Tushar Swamy, Annus Zulfiqar, Luigi Nardi +2
Support for Machine Learning (ML) applications in networks has significantly improved over the last decade. The availability of public datasets and programmable switching fabrics (…
Joint Entropy Search for Maximally-Informed Bayesian Optimization
Carl Hvarfner, Frank Hutter, Luigi Nardi
Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Searc…
BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Carl Hvarfner, Danny Stoll, Artur Souza +3
Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sampl…
Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration
Matthias Mayr, Faseeh Ahmad, Konstantinos Chatzilygeroudis +2
In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handl…
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Kenan Šehić, Alexandre Gramfort, Joseph Salmon +1
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…
Learning of Parameters in Behavior Trees for Movement Skills
Matthias Mayr, Konstantinos Chatzilygeroudis, Faseeh Ahmad +2
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL…
Bayesian Optimization with a Prior for the Optimum
Artur Souza, Luigi Nardi, Leonardo B. Oliveira +3
While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts. This causes BO to was…
Polystore++: Accelerated Polystore System for Heterogeneous Workloads
Rekha Singhal, Nathan Zhang, Luigi Nardi +2
Modern real-time business analytic consist of heterogeneous workloads (e.g, database queries, graph processing, and machine learning). These analytic applications need programming…
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning
Artur Souza, Leonardo B. Oliveira, Sabine Hollatz +5
Serial crystallography is the field of science that studies the structure and properties of crystals via diffraction patterns. In this paper, we introduce a new serial crystallogra…
Practical Design Space Exploration
Luigi Nardi, David Koeplinger, Kunle Olukotun
Multi-objective optimization is a crucial matter in computer systems design space exploration because real-world applications often rely on a trade-off between several objectives.…
SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
Bruno Bodin, Harry Wagstaff, Sajad Saeedi +9
SLAM is becoming a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify…
Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality
Sajad Saeedi, Bruno Bodin, Harry Wagstaff +23
Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is cent…
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
Cody Coleman, Daniel Kang, Deepak Narayanan +7
Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the…
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper
Luigi Nardi, Bruno Bodin, Sajad Saeedi +3
In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future. The goal of this exploration is to re…
A structure-exploiting numbering algorithm for finite elements on extruded meshes, and its performance evaluation in Firedrake
Gheorghe-Teodor Bercea, Andrew T. T. McRae, David A. Ham +5
We present a generic algorithm for numbering and then efficiently iterating over the data values attached to an extruded mesh. An extruded mesh is formed by replicating an existing…