782 research outputs found
10381 Summary and Abstracts Collection -- Robust Query Processing
Dagstuhl seminar 10381 on robust query processing (held 19.09.10 -
24.09.10) brought together a diverse set of researchers and practitioners
with a broad range of expertise for the purpose of fostering discussion
and collaboration regarding causes, opportunities, and solutions for
achieving robust query processing.
The seminar strove to build a unified view across
the loosely-coupled system components responsible for
the various stages of database query processing.
Participants were chosen for their experience with database
query processing and, where possible, their prior work in academic
research or in product development towards robustness in database query
processing.
In order to pave the way to motivate, measure, and protect future advances
in robust query processing, seminar 10381 focused on developing tests
for measuring the robustness of query processing.
In these proceedings, we first review the seminar topics, goals,
and results, then present abstracts or notes of some of the seminar break-out
sessions.
We also include, as an appendix,
the robust query processing reading list that
was collected and distributed to participants before the seminar began,
as well as summaries of a few of those papers that were
contributed by some participants
The feasibility of using feature-flow and label transfer system to segment medical images with deformed anatomy in orthopedic surgery
In computer-aided surgical systems, to obtain high fidelity three-dimensional models, we require accurate segmentation of medical images. State-of-art medical image segmentation methods have been used successfully in particular applications, but they have not been demonstrated to work well over a wide range of deformities. For this purpose, I studied and evaluated medical image segmentation using the feature-flow based Label Transfer System described by Liu and colleagues. This system has produced promising results in parsing images of natural scenes. Its ability to deal with variations in shapes of objects is desirable. In this paper, we altered this system and assessed its feasibility of automatic segmentation. Experiments showed that this system achieved better recognition rates than those in natural-scene parsing applications, but the high recognition rates were not consistent across different images. Although this system is not considered clinically practical, we may improve it and incorporate it with other medical segmentation tools
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization
This paper introduces a modular framework for Mixed-variable and
Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic
benchmarking and standardized evaluation in the field. Current MCBO papers
often introduce non-diverse or non-standard benchmarks to evaluate their
methods, impeding the proper assessment of different MCBO primitives and their
combinations. Additionally, papers introducing a solution for a single MCBO
primitive often omit benchmarking against baselines that utilize the same
methods for the remaining primitives. This omission is primarily due to the
significant implementation overhead involved, resulting in a lack of controlled
assessments and an inability to showcase the merits of a contribution
effectively. To overcome these challenges, our proposed framework enables an
effortless combination of Bayesian Optimization components, and provides a
diverse set of synthetic and real-world benchmarking tasks. Leveraging this
flexibility, we implement 47 novel MCBO algorithms and benchmark them against
seven existing MCBO solvers and five standard black-box optimization algorithms
on ten tasks, conducting over 4000 experiments. Our findings reveal a superior
combination of MCBO primitives outperforming existing approaches and illustrate
the significance of model fit and the use of a trust region. We make our MCBO
library available under the MIT license at
\url{https://github.com/huawei-noah/HEBO/tree/master/MCBO}
Generative Pretraining for Black-Box Optimization
Many problems in science and engineering involve optimizing an expensive
black-box function over a high-dimensional space. For such black-box
optimization (BBO) problems, we typically assume a small budget for online
function evaluations, but also often have access to a fixed, offline dataset
for pretraining. Prior approaches seek to utilize the offline data to
approximate the function or its inverse but are not sufficiently accurate far
from the data distribution. We propose BONET, a generative framework for
pretraining a novel black-box optimizer using offline datasets. In BONET, we
train an autoregressive model on fixed-length trajectories derived from an
offline dataset. We design a sampling strategy to synthesize trajectories from
offline data using a simple heuristic of rolling out monotonic transitions from
low-fidelity to high-fidelity samples. Empirically, we instantiate BONET using
a causally masked Transformer and evaluate it on Design-Bench, where we rank
the best on average, outperforming state-of-the-art baselines.Comment: International Conference for Machine Learning 2023 NeurIPS Workshop
for Foundational Models for Decision Making (Oral) 202
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