In this paper, we present a novel Bayesian optimization method named Conditional Local Bayesian Optimization (CLBO) designed specifically to address challenges in optimizing Automated Machine Learning (AutoML) tasks. Inspired by a controller-responder architecture, our method leverages a controller that selects promising pipelines based on an acquisition function. Local responders employ Bayesian optimization to refine the search within sub-spaces. CLBO introduces a progressive budget system, which dynamically allocates the optimization budget. Our method utilizes a group of surrogate models to initially optimize a simpler objective function. Notably, CLBO outperforms current state-of-the-art algorithms on 35 classification datasets from the OpenML repository. Finally, CLBO reports unbiased performance estimates through the use of a performance correction method.
In facility location problems we seek to locate a set of facilities in an area, where clients may be present, so that some criterion is optimized. In the p-median problem we seek to minimize the sum of distances between demand points and their nearest facility, whereas in the p-dispersion problem we seek to maximize the closest distance between any two facilities. Recently, a variant of p-dispersion where distance constraints exist between facilities was studied from a Constraint Programming (CP) and Integer Linear Programming (ILP) perspective. An incomplete CP solver that uses a greedy heuristic to prune branches during search was shown to significantly outperform the ILP solver Gurobi and the CP solver OR-Tools in terms of execution time. Following that work, we consider a variant of the p-median problem where distance constraints exist between facilities and between facilities and demand points. This problem can be used to model the requirements that arise when locating semi-obnoxious facilities. We first introduce ILP and CP models and implement them in Gurobi and OR-Tools. Then, we demonstrate how a heuristic CP solver can be developed and applied on the p-median problem with distance constraints, comparing it to Gurobi and OR-Tools.
In this work we investigate zero shot learning in side effects prediction of a pair of drugs. Often combined usage of drugs has undesired side effects which have not yet been observed during clinical trials. Whereas the prediction of side effects can be formulated as a supervised machine learning problem, there are cases where there are no samples for certain sides effects, i.e. for certain classes. Thus, a zero shot learning approach can be pursued. We adapted an existing zero shot learning approach to the problem of prediction of side effects of pairs of drugs. One particular aspect of the problem is that a pair of drugs may have multiple side effects, which turns the problem into a multilabel, zero shot prediction. Finally, extensive experimentation has been conducted to gain insights.
This paper presents an improved approach to wildfire protection by integrating heterogeneous data sources under a novel fire detection ontology. The introduced technique automatically unifies data from IoT devices into a single Semantic Knowledge Graph (SemKG), improving user experience by removing the need for users to examine several data sources. To make easier quick and well-informed decision-making, the system generates real-time notifications with different degrees of severity. The validation of the introduced technique has already taken place in various pilot sites of the SILVANUS H2020 project, which focus on developing a climate-resilient forest management platform to prevent and suppress wildfires, involving 49 partners from the European Union, Brazil, Indonesia, and Australia. In this paper, we demonstrate the effectiveness of this approach through scenarios of active fire detection and gas leakage.
The integration of machine learning (ML) within the realm of nanomanufacturing processes, specifically through the applications of thickness prediction during deposition and pattern fidelity during etching, may present transformative potential. This paper discusses two ML-based methodologies that enhance the precision and efficiency of these critical processes. In the context of etching, we revisit the 3D geometrical modeling of etch-induced Line Edge Roughness (LER) transfer from photoresist lines to the substrate, refining previous models to account for realistic three-dimensional surface topographies. Meanwhile, in the deposition segment, we employ a deep neural network (DNN) to predict the thickness of films deposited on rough substrates using binarized top-down Scanning Electron Microscopy (SEM) images. The outcomes demonstrate how ML-based methodologies not only predict but also potentially control the nanofabrication parameters, leading to improved manufacturing outcomes.
