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Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators 13

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Evolutionary Dynamic Optimization-Based Calibration Framewor...

13th IEEE Congress on Evolutionary computation, CEC 2024

作者： Yang, Zhenhua Zhong, Muyao Yang, Peng Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Statistics and Data Science Shenzhen518055 China

ISBN: (纸本)9798350308365

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration. © 2024 IEEE.

关键词： Financial markets

Complexity of Inconsistency-Tolerant Query Answering in Datalog+/- Under Preferred Repairs 20

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Complexity of Inconsistency-Tolerant Query Answering in Data...

20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023

作者： Lukasiewicz, Thomas Malizia, Enrico Molinaro, Cristian Institute of Logic and Computation Vienna University of Technology Austria Department of Computer Science University of Oxford United Kingdom DISI University of Bologna Italy DIMES University of Calabria Italy

ISBN: (纸本)9781956792027

Inconsistency-tolerant semantics have been proposed to provide meaningful ontological query answers even in the presence of inconsistencies. Several such semantics rely on the notion of a repair, which is a "maximal" consistent subset of the database, where different maximality criteria might be adopted depending on the application at hand. Previous work in the context of Datalog± has considered only the subset and cardinality maximality criteria. We take here a step further and study inconsistency-tolerant semantics under maximality criteria based on weights and priority levels. We provide a thorough complexity analysis for a wide range of existential rule languages and for several complexity measures. © 2023 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved

关键词： Semantics

Effects of Including Optimal Solutions into Initial Population on Evolutionary Multiobjective Optimization 23

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Effects of Including Optimal Solutions into Initial Populati...

2023 Genetic and Evolutionary computation Conference, GECCO 2023

作者： Gong, Cheng Nan, Yang Pang, Lie Meng Zhang, Qingfu Ishibuchi, Hisao Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798400701191

A long-standing question in the evolutionary multi-objective (EMO) community is how to generate a good initial population for EMO algorithms. Intuitively, as the starting point of optimization, a good initial population can have positive effects on the performance of EMO algorithms. However, in most existing EMO algorithms, one of the commonly-used initialization methods is to randomly generate a set of solutions as an initial population. One possible approach to improve random initialization is to include one or more Pareto optimal (near Pareto optimal) solution(s) in the initial population, which are expected to provide useful information and knowledge on the optimized problem. In this paper, to investigate the effectiveness of this initialization idea, we examine and quantify the effects of including one or more Pareto optimal solution(s) in the initial population on the performance of EMO algorithms. Experimental results demonstrate that it is worthwhile to first obtain and then include some Pareto optimal solutions in the initial population. Through a number of experiments and algorithm behavior analysis, this study provides supports and insights into EMO algorithm design and motivates further research on population initialization for EMO algorithms. © 2023 ACM.

关键词： Evolutionary algorithms

VAE-KRnet and its Applications to Variational Bayes

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Communications in computational Physics 2022年第4期31卷 1049-1082页

作者： Xiaoliang Wan Shuangqing Wei Department of Mathematics Center for Computation and TechnologyLouisiana State UniversityBaton Rouge 70803USA Division of Electrical&Computer Engineering Louisiana State UniversityBaton Rouge 70803USA

In this work,we have proposed a generative model,called VAE-KRnet,for density estimation or approximation,which combines the canonical variational autoencoder(VAE)with our recently developed flow-based generativemodel,called *** is used as a dimension reduction technique to capture the latent space,and KRnet is used to model the distribution of the latent *** a linear model between the data and the latent variable,we show that VAE-KRnet can be more effective and robust than the canonical ***-KRnet can be used as a density model to approximate either data distribution or an arbitrary probability density function(PDF)known up to a ***-KRnet is flexible in terms of *** the number of dimensions is relatively small,KRnet can effectively approximate the distribution in terms of the original random *** high-dimensional cases,we may use VAE-KRnet to incorporate dimension *** important application of VAE-KRnet is the variational Bayes for the approximation of the posterior *** variational Bayes approaches are usually based on the minimization of the Kullback-Leibler(KL)divergence between the model and the *** highdimensional distributions,it is very challenging to construct an accurate densitymodel due to the curse of dimensionality,where extra assumptions are often introduced for *** instance,the classical mean-field approach assumes mutual independence between dimensions,which often yields an underestimated variance due to *** alleviate this issue,we include into the loss the maximization of the mutual information between the latent random variable and the original random variable,which helps keep more information from the region of low density such that the estimation of variance is *** experiments have been presented to demonstrate the effectiveness of our model.

关键词： Deep learning variational Bayes uncertainty quantification Bayesian inverse problems generative modeling

Online busy time scheduling with flexible jobs

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arXiv 2024年

作者： Albers, Susanne van der Heijden, G. Wessel Technical University of Munich TUM School of Computation Information and Technology Department of Computer Science Germany Technical University of Munich TUM School of Computation Information and Technology Germany

We present several competitive ratios for the online busy time scheduling problem with flexible jobs. The busy time scheduling problem is a fundamental scheduling problem motivated by energy efficiency with the goal of minimizing the total time that machines with multiple processors are enabled. In the busy time scheduling problem, an unbounded number of machines is given, where each machine has g processors. No more than g jobs can be scheduled simultaneously on each machine. A machine consumes energy whenever at least one job is scheduled at any time on the machine. Scheduling a single job at some time t consumes the same amount of energy as scheduling g jobs at time t. In the online setting, jobs are revealed when they are released. We consider the cases where g is unbounded and bounded. In this paper, we revisit the bounds of the unbounded general setting from the literature and tighten it significantly. We also consider agreeable jobs. For the bounded setting, we show a tightened upper bound. Furthermore, we show the first constant competitive ratio in the bounded setting that does not require lookahead. © 2024, CC BY.

关键词： Energy efficiency

Osteoporotic Vertebral Fracture Classification in X-rays Based on a Multi-modal Semantic Consistency Network

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Journal of Bionic Engineering 2022年第6期19卷 1816-1829页

作者： Yuzhao Wang Tian Bai Tong Li Lan Huang College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Department of Orthopedics The Second Hospital of Jilin UniversityChangchun130012China

Osteoporotic Vertebral Fracture(OVFs)is a common lumbar spine disorder that severely affects the health of *** a clear bone blocks boundary,CT images have gained obvious advantages in OVFs *** with CT images,X-rays are faster and more inexpensive but often leads to misdiagnosis and miss-diagnosis because of the overlapping *** how to transfer CT imaging advantages to achieve OVFs classification in X-rays is *** this purpose,we propose a multi-modal semantic consistency network which could do well X-ray OVFs classification by transferring CT semantic consistency *** from existing methods,we introduce a feature-level mix-up module to get the domain soft labels which helps the network reduce the domain offsets between CT and *** the meanwhile,the network uses a self-rotation pretext task on both CT and X-ray domains to enhance learning the high-level semantic invariant *** employ five evaluation metrics to compare the proposed method with the state-of-the-art *** final results show that our method improves the best value of AUC from 86.32 to 92.16%.The results indicate that multi-modal semantic consistency method could use CT imaging features to improve osteoporotic vertebral fracture classification in X-rays effectively.

关键词： Osteoporotic vertebral fracture classification Cross-modality Unsupervised domain adaptation Transfer learning Convolutional neural network computer-aided diagnosis

A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy

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arXiv 2024年

作者： Meisenbacher, Stephen Chevli, Maulik Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Garching Germany

Applications of Differential Privacy (DP) in NLP must distinguish between the syntactic level on which a proposed mechanism operates, often taking the form of word-level or document-level privatization. Recently, several word-level Metric Differential Privacy approaches have been proposed, which rely on this generalized DP notion for operating in word embedding spaces. These approaches, however, often fail to produce semantically coherent textual outputs, and their application at the sentence- or document-level is only possible by a basic composition of word perturbations. In this work, we strive to address these challenges by operating between the word and sentence levels, namely with collocations. By perturbing n-grams rather than single words, we devise a method where composed privatized outputs have higher semantic coherence and variable length. This is accomplished by constructing an embedding model based on frequently occurring word groups, in which unigram words co-exist with bi- and trigram collocations. We evaluate our method in utility and privacy tests, which make a clear case for tokenization strategies beyond the word level. © 2024, CC BY.

关键词： Embeddings

Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text

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arXiv 2024年

作者： Meisenbacher, Stephen Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Garching Germany

The study of Differential Privacy (DP) in Natural Language Processing often views the task of text privatization as a rewriting task, in which sensitive input texts are rewritten to hide explicit or implicit private information. In order to evaluate the privacy-preserving capabilities of a DP text rewriting mechanism, empirical privacy tests are frequently employed. In these tests, an adversary is modeled, who aims to infer sensitive information (e.g., gender) about the author behind a (privatized) text. Looking to improve the empirical protections provided by DP rewriting methods, we propose a simple post-processing method based on the goal of aligning rewritten texts with their original counterparts, where DP rewritten texts are rewritten again. Our results show that such an approach not only produces outputs that are more semantically reminiscent of the original inputs, but also texts which score on average better in empirical privacy evaluations. Therefore, our approach raises the bar for DP rewriting methods in their empirical privacy evaluations, providing an extra layer of protection against malicious adversaries. © 2024, CC BY.

关键词： Privatization

DP-MLM: Differentially Private Text Rewriting Using Masked Language Models

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arXiv 2024年

作者： Meisenbacher, Stephen Chevli, Maulik Vladika, Juraj Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Garching Germany

The task of text privatization using Differential Privacy has recently taken the form of text rewriting, in which an input text is obfuscated via the use of generative (large) language models. While these methods have shown promising results in the ability to preserve privacy, these methods rely on autoregressive models which lack a mechanism to contextualize the private rewriting process. In response to this, we propose DP-MLM, a new method for differentially private text rewriting based on leveraging masked language models (MLMs) to rewrite text in a semantically similar and obfuscated manner. We accomplish this with a simple contextualization technique, whereby we rewrite a text one token at a time. We find that utilizing encoder-only MLMs provides better utility preservation at lower Ε levels, as compared to previous methods relying on larger models with a decoder. In addition, MLMs allow for greater customization of the rewriting mechanism, as opposed to generative approaches. We make the code for DP-MLM public and reusable, found at https://***/sjmeis/DPMLM. © 2024, CC BY.

关键词： Privatization