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Projective preferential bayesian optimization 37

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Projective preferential bayesian optimization

37th International Conference on Machine Learning, ICML 2020

作者： Mikkola, Petrus Todorovic, Milica Jarvi, Jari Rinke, Patrick Kaski, Samue Helsinki Institute for Information Technology HIIT Department of Computer Science Aalto University Espoo Finland Department of Applied Physics Aalto University Espoo Finland University of Manchester United Kingdom

ISBN: (纸本)9781713821120

Bayesian optimization is an effective method for fnding extrema of a black-box function. We propose a new type of Bayesian optimization for learning user preferences in high-dimensional spaces. The central assumption is that the underlying objective function cannot be evaluated directly, but instead a minimizer along a projection can be queried, which we call a projective preferential query. The form of the query allows for feedback that is natural for a human to give, and which enables interaction. This is demonstrated in a user experiment in which the user feedback comes in the form of optimal position and orientation of a molecule adsorbing to a surface. We demonstrate that our framework is able to fnd a global minimum of a high-dimensional black-box function, which is an infeasible task for existing preferential Bayesian optimization frameworks that are based on pairwise comparisons. © 2020 37th International Conference on Machine Learning, ICML 2020. All rights reserved.

关键词： Global optimization

Bounds on the ground state energy of quantum p-spin Hamiltonians

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arXiv 2024年

作者： Anschuetz, Eric R. Gamarnik, David Kiani, Bobak T. Institute for Quantum Information and Matter Caltech Walter Burke Institute for Theoretical Physics Caltech MIT Center for Theoretical Physics MIT United States Operations Research Center Statistics and Data Science Center Sloan School of Management MIT United States John A. Paulson School of Engineering and Applied Sciences Department of Electrical Engineering and Computer Science MIT Harvard United States

We consider the problem of estimating the ground state energy of quantum p-local spin glass random Hamiltonians, the quantum analogues of widely studied classical spin glass models. Our main result shows that the maximum energy achievable by product states has a well-defined limit (for even p) as n → ∞ and is Eproduct∗ = √2 log p in the limit of large p. This value is interpreted as the maximal energy of a much simpler so-called Random Energy Model, widely studied in the setting of classical spin glasses. The proof of the limit existing follows from an extension of Fekete’s Lemma after we demonstrate near super-additivity of the (normalized) quenched free energy. The proof of the value follows from a second moment method on the number of states achieving a given energy when restricting to an ǫ-net of product states. Furthermore, we relate the maximal energy achieved over all states to a p-dependent constant γ (p), which is defined by the degree of violation of a certain asymptotic independence ansatz over graph matchings. We show that the maximal energy achieved by all states E∗ (p) in the limit of large n is at most pγ (p)Eproduct∗ . We also prove using Lindeberg’s interpolation method that the limiting E∗ (p) is robust with respect to the choice of the randomness and, for instance, also applies to the case of sparse random Hamiltonians. This robustness in the randomness extends to a wide range of random Hamiltonian models including SYK and random quantum max-cut. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

Semantic Data Representation for Explainable Windows Malware Detection Models

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arXiv 2024年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kľuka, Ján Bisták, Tomáš Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava Slovakia

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive domains of human interest. They are becoming increasingly important also in the areas that have been until very recently dominated by subsymbolic knowledge representation and machine-learning (ML) based data processing. One such area is information security, and specifically, malware detection. We thus propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE – the Windows binary format) malware files. This ontology is inspired by the structure of the EMBER dataset, which focuses on the static malware analysis of PE files. With this proposal, we hope to provide a unified semantic representation for the existing and future PE-malware datasets and facilitate the application of symbolic, neuro-symbolic, or otherwise explainable approaches in the PE-malware-detection domain, which may produce interpretable results described by the terms defined in our ontology. In addition, we also publish semantically treated EMBER data, including fractional datasets, to support the reproducibility of experiments on EMBER. We supplement our work with a preliminary case study, conducted using concept learning, to show the general feasibility of our approach. While we were not able to match the precision of the state-of-the-art ML tools, the learned malware discriminators were interesting and highly interpretable. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology

Multi-Tap Canceller for Wi-Fi Communication in Band with Fill Duplex 6

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Multi-Tap Canceller for Wi-Fi Communication in Band with Fil...

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Saha, Supratim Gautam, Rajni Belwal, Pravesh Hemelatha, S. Kannimuthu, S. Aher, Vijaya N. Swami Vivekanand Subharti University Electronics and Communication Engineering Meerut India School of Basic & Applied Sciences K. R. Mangalam University Department of Physics Gurugram India Dev Bhoomi Uttarakhand University Electronics and Communications Engineering Uttarakhand India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Karpagam College of Engineering Department of Information Technology Coimbatore India Vishwakarma Institute of Information Technology E&TC Pune India

ISBN: (纸本)9798350304480

Since they involve the appropriate reduction of self-interference induced by the co-located transmitters, in-band full-duplex wirelessly network is difficult to implement. In order to increase system separation, the straight antennas coupling and multichip effects that make up a normal disturbance channel are both cancelled using a novel input time line RF recycled content structure shown in this work. A four-tap recycled content prototype had been evaluated under various settings and discovered to provide, in isolation situations, an average of 30 dB of signal cancelation above a 30 MHz band centered at 2.45 GHz. The canceller increased the overall analog seclusion to 90 dB for such cases when used in conjunction with only Omni-directional high-isolation antennas. The canceller reduced a +30 dbm OFDM in an enclosed environment. Along a 20 MHz band with a 2.45 GHz center, the device reduced the noise by 22 dB and generated 78 dB of overall analog seclusion. This thorough evaluation establishes the degree of analogue interference cancellation that may be anticipated for the various situations taken into account, as well as the effectiveness constraints of an optimized multi RF attenuator. © 2023 IEEE.

关键词： Adaptive canceller analog cancellation IBFD in-band full-duplex wireless communication interference cancellation RF cancellation simultaneous transmit and receive STAR

Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization

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arXiv 2025年

作者： Maus, Natalie Kim, Kyurae Zeng, Yimeng Jones, Haydn Thomas Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of T black-box objective functions, f1, . . . fT , simultaneously. Traditional approaches often seek a single Pareto-optimal set that balances trade-offs among all objectives. In this work, we introduce a novel problem setting that departs from this paradigm: finding a smaller set of K solutions, where K 1, . . . fT , there is at least one good solution. A motivating example for this problem setting occurs in drug design. For example, we may have T pathogens and aim to identify a set of K © 2025, CC BY-SA.

关键词： Pathogens

Large Scale Multi-Task Bayesian Optimization with Large Language Models

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arXiv 2025年

作者： Zeng, Yimeng Maus, Natalie Jones, Haydn Thomas Tao, Jeffrey Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Marcus, Ryan Bastani, Osbert Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using multi-task Gaussian processes or deep kernel transfer exist, the performance improvement is marginal when scaling to more than a moderate number of tasks. We introduce a novel approach leveraging large language models (LLMs) to learn from, and improve upon, previous optimization trajectories, scaling to approximately 2000 distinct tasks. Specifically, we propose an iterative framework in which an LLM is fine-tuned using the high quality solutions produced by BayesOpt to generate improved initializations that accelerate convergence for future optimization tasks based on previous search trajectories. We evaluate our method on two distinct domains: database query optimization and antimicrobial peptide design. Results demonstrate that our approach creates a positive feedback loop, where the LLM’s generated initializations gradually improve, leading to better optimization performance. As this feedback loop continues, we find that the LLM is eventually able to generate solutions to new tasks in just a few shots that are better than the solutions produced by "from scratch" by Bayesian optimization while simultaneously requiring significantly fewer oracle calls. © 2025, CC BY-SA.

关键词： Structured Query Language

Maximal quantum interaction between free electrons and photons

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arXiv 2024年

作者： Xie, Zetao Chen, Zeling Li, Hao Yan, Qinghui Chen, Hongsheng Lin, Xiao Kaminer, Ido Miller, Owen D. Yang, Yi Department of Physics HK Institute of Quantum Science and Technology The University of Hong Kong Pokfulam Hong Kong Department of Applied Physics Energy Sciences Institute Yale University New HavenCT06520 United States Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Solid State Institute Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa32000 Israel

The emerging field of free-electron quantum optics enables electron-photon entanglement and holds the potential for generating nontrivial photon states for quantum information processing. Although recent experimental studies have entered the quantum regime, rapid theoretical developments predict that qualitatively unique phenomena only emerge beyond a certain interaction strength. It is thus pertinent to identify the maximal electron-photon interaction strength and the materials, geometries, and particle energies that enable one to approach it. We derive an upper limit to the quantum vacuum interaction strength between free electrons and single-mode photons, which illuminates the conditions for the strongest interaction. Crucially, we obtain an explicit energy selection recipe for electrons and photons to achieve maximal interaction at arbitrary separations and identify two optimal regimes favoring either fast or slow electrons over those with intermediate velocities. We validate the limit by analytical and numerical calculations on canonical geometries and provide near-optimal designs indicating the feasibility of strong quantum interactions. Our findings offer fundamental intuition for maximizing the quantum interaction between free electrons and photons and provide practical design rules for future experiments on electron-photon and electron-mediated photon-photon entanglement. They should also enable the evaluation of key metrics for applications such as the maximum power of free-electron radiation sources and the maximum acceleration gradient of dielectric laser accelerators. © 2024, CC BY.

关键词： Photons

Non-homotopic Loops with a Bounded Number of Pairwise Intersections 29th

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Non-homotopic Loops with a Bounded Number of Pairwise Int...

29th International Symposium on Graph Drawing and Network Visualization, GD 2021

作者： Blažej, Václav Opler, Michal Šileikis, Matas Valtr, Pavel Faculty of Information Technology Czech Technical University in Prague Prague Czech Republic Computer Science Institute Charles University Prague Czech Republic Czech Academy of Sciences Institute of Computer Science Prague Czech Republic Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (纸本)9783030929305

Let Vn be a set of n points in the plane and let x∉ Vn. An x-loop is a continuous closed curve not containing any point of Vn. We say that two x-loops are non-homotopic if they cannot be transformed continuously into each other without passing through a point of Vn. For n= 2, we give an upper bound eO(k) on the maximum size of a family of pairwise non-homotopic x-loops such that every loop has fewer than k self-intersections and any two loops have fewer than k intersections. The exponent O(k) is asymptotically tight. The previous upper bound 2(2k)4 was proved by Pach et al. [6]. We prove the above result by proving the asymptotic upper bound eO(k) for a similar problem when x∈ Vn, and by proving a close relation between the two problems. © 2021, Springer Nature Switzerland AG.

关键词： Drawing (graphics)

Learning Relevant Contextual Variables Within Bayesian Optimization

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arXiv 2023年

作者： Martinelli, Julien Bharti, Ayush Tiihonen, Armi John, S.T. Filstroff, Louis Sloman, Sabina J. Rinke, Patrick Kaski, Samuel Inserm Bordeaux Population Health Vaccine Research Institute Université de Bordeaux Inria Bordeaux Sud-ouest France Department of Computer Science Aalto University Helsinki Finland Department of Applied Physics Aalto University Helsinki Finland Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France Department of Computer Science University of Manchester Manchester United Kingdom

Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions. However, the relevance of contextual variables is not necessarily known beforehand. Moreover, contextual variables can sometimes be optimized themselves at an additional cost, a setting overlooked by current CBO algorithms. Cost-sensitive CBO would simply include optimizable contextual variables as part of the design variables based on their cost. Instead, we adaptively select a subset of contextual variables to include in the optimization, based on the trade-off between their relevance and the additional cost incurred by optimizing them compared to leaving them to be determined by the environment. We learn the relevance of contextual variables by sensitivity analysis of the posterior surrogate model while minimizing the cost of optimization by leveraging recent developments on early stopping for BO. We empirically evaluate our proposed Sensitivity-Analysis-Driven Contextual BO (SADCBO) method against alternatives on both synthetic and real-world experiments, together with extensive ablation studies, and demonstrate a consistent improvement across examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensitivity analysis