1 code implementation • 3 Feb 2024 • Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi
High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization algorithms.
1 code implementation • 24 Nov 2023 • Carl Hvarfner, Frank Hutter, Luigi Nardi
The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines.
1 code implementation • 5 Oct 2023 • Erik Orm Hellsten, Carl Hvarfner, Leonard Papenmeier, Luigi Nardi
We propose a group testing approach to identify active variables to facilitate efficient optimization in these domains.
1 code implementation • NeurIPS 2023 • 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 with mixed and combinatorial input spaces.
2 code implementations • NeurIPS 2023 • Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance.
no code implementations • 16 May 2023 • Simon Kristoffersson Lind, Rudolph Triebel, Luigi Nardi, Volker Krueger
It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions.
2 code implementations • 22 Apr 2023 • 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 applications, for example, in the life sciences, neural architecture search, and robotics.
no code implementations • NeurIPS 2023 • Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi
Gaussian processes are the model of choice in Bayesian optimization and active learning.
1 code implementation • 1 Dec 2022 • Erik Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjolstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs.
no code implementations • 14 Sep 2022 • Zahra Ramezani, Kenan Šehić, Luigi Nardi, Knut Åkesson
For some of the benchmark problems, the choice of acquisition function clearly affects the number of simulations needed for successful falsification.
2 code implementations • 9 Jun 2022 • Carl Hvarfner, Frank Hutter, Luigi Nardi
As a light-weight approach with superior results, JES provides a new go-to acquisition function for Bayesian optimization.
1 code implementation • 23 Apr 2022 • Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
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.
1 code implementation • 18 Mar 2022 • Matthias Mayr, Faseeh Ahmad, Konstantinos Chatzilygeroudis, Luigi Nardi, Volker Krueger
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
1 code implementation • 4 Nov 2021 • Kenan Šehić, Alexandre Gramfort, Joseph Salmon, Luigi Nardi
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.
no code implementations • ICLR 2022 • Carl Hvarfner, Danny Stoll, Artur Souza, Luigi Nardi, Marius Lindauer, Frank Hutter
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.
1 code implementation • 27 Sep 2021 • Matthias Mayr, Konstantinos Chatzilygeroudis, Faseeh Ahmad, Luigi Nardi, Volker Krueger
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error.
no code implementations • 28 Sep 2020 • Artur Souza, Luigi Nardi, Leonardo Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts.
no code implementations • 25 Jun 2020 • Artur Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
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.
no code implementations • 24 May 2019 • Rekha Singhal, Nathan Zhang, Luigi Nardi, Muhammad Shahbaz, Kunle Olukotun
Modern real-time business analytic consist of heterogeneous workloads (e. g, database queries, graph processing, and machine learning).
1 code implementation • 26 Apr 2019 • Artur Souza, Leonardo B. Oliveira, Sabine Hollatz, Matt Feldman, Kunle Olukotun, James M. Holton, Aina E. Cohen, Luigi Nardi
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.
no code implementations • 11 Oct 2018 • Luigi Nardi, David Koeplinger, Kunle Olukotun
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.
no code implementations • 27 Sep 2018 • Artur Souza, Leonardo B. Oliveira, Sabine Hollatz, Matt Feldman, Kunle Olukotun, James M. Holton, Aina E. Cohen, Luigi Nardi
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.
2 code implementations • 20 Aug 2018 • Sajad Saeedi, Bruno Bodin, Harry Wagstaff, Andy Nisbet, Luigi Nardi, John Mawer, Nicolas Melot, Oscar Palomar, Emanuele Vespa, Tom Spink, Cosmin Gorgovan, Andrew Webb, James Clarkson, Erik Tomusk, Thomas Debrunner, Kuba Kaszyk, Pablo Gonzalez-de-Aledo, Andrey Rodchenko, Graham Riley, Christos Kotselidis, Björn Franke, Michael F. P. O'Boyle, Andrew J. Davison, Paul H. J. Kelly, Mikel Luján, Steve Furber
Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge.
no code implementations • 4 Jun 2018 • Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Re, Matei Zaharia
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.
no code implementations • 2 Feb 2017 • Luigi Nardi, Bruno Bodin, Sajad Saeedi, Emanuele Vespa, Andrew J. Davison, Paul H. J. Kelly
In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future.
no code implementations • 15 Sep 2015 • M. Zeeshan Zia, Luigi Nardi, Andrew Jack, Emanuele Vespa, Bruno Bodin, Paul H. J. Kelly, Andrew J. Davison
SLAM has matured significantly over the past few years, and is beginning to appear in serious commercial products.
3 code implementations • 17 Aug 2015 • André Dietrich, Sebastian Zug, Luigi Nardi, Jörg Kaiser
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.
3 code implementations • 8 Oct 2014 • Luigi Nardi, Bruno Bodin, M. Zeeshan Zia, John Mawer, Andy Nisbet, Paul H. J. Kelly, Andrew J. Davison, Mikel Luján, Michael F. P. O'Boyle, Graham Riley, Nigel Topham, Steve Furber
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.