Study shows non-systematic bias in customer satisfaction surveys limits data value.
problem Non-systematic bias in customer satisfaction surveys limits data value.
method Used real customer satisfaction survey data of a large retail bank to show the irreducible error and suggest thoughtful survey design methods.
result A thoughtful survey design can reduce non-systematic error in customer satisfaction surveys.
This paper surveys enterprise financial risk analysis from Big Data and LLMs perspectives.
problem Predicting future financial risk of enterprises.
method Systematic literature review of enterprise financial risk analysis approaches from Big Data and LLMs perspectives.
result Offers a holistic synthesis of research methods and key insights.
Survey of diffusion models for time series forecasting.
problem Lack of systematic taxonomy for diffusion models in time series forecasting.
method Introduction and review of standard diffusion models, their variants, and their adaptation to time series tasks.
result Provides a comprehensive categorization and summary of diffusion models for time series forecasting.
Systematic review finds reinforcement learning enhances financial tech performance.
problem Improving financial tech performance through reinforcement learning.
method Systematic review using PRISMA technique.
result RL-based strategies outperform other algorithms in financial tech.
Survey of integrating planning and learning in model-based reinforcement learning.
problem Sequential decision making in AI, formalized as MDP optimization.
method Systematic coverage of dynamics model learning and planning-learning integration.
result Broad conceptual overview of model-based reinforcement learning.
Survey on geometric properties of special minimal surfaces.
problem Understanding [ φ , e ⃗ 3 ] [\varphi,\vec{e}_{3}] [ φ , e 3 ] -minimal surfaces in R 3 \mathbb{R}^{3} R 3 . method Systematic geometric study of [ φ , e ⃗ 3 ] [\varphi,\vec{e}_{3}] [ φ , e 3 ] -minimal surfaces. result Fundamental results in the theory of [ φ , e ⃗ 3 ] [\varphi,\vec{e}_{3}] [ φ , e 3 ] -minimal surfaces. Survey on rational curves on complex surfaces, highlighting different approaches.
problem Existence of rational curves on complex surfaces.
method Classification of complex surfaces and systematic study of rational curves in each class.
result Highlighting the different approaches to study rational curves on complex surfaces.
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computa…
We present a Bayesian framework for estimating the customer lifetime value (CLV) and the customer equity (CE) based on the purchasing behavior deducible from the market surveys on customer purchasing behavior. The proposed framework systematically addresses the challenges faced when the future value of customers is est…
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically assess explainable systems along five key dimensions: functional, operational, usab…
Survey of Willmore surfaces in spheres using DPW method.
problem Global and local properties of Willmore surfaces in spheres.
method DPW method for conformal Gauss map.
result Characterizations of minimal surfaces and Willmore deformations.
Survey on modeling event sequences through temporal processes.
problem Modeling phenomena with sequences of events over continuous time.
method Probabilistic models based on point processes, categorized into simple, marked, and spatio-temporal.
result Analysis of existing approaches and their applicability to prediction and modeling.
Survey of deep learning methods for medical anomaly detection.
problem Medical anomaly detection using machine learning.
method Thorough review of deep learning techniques across various medical domains.
result Comparison and contrast of deep learning models and their limitations.
Discrete differential geometry aims to develop discrete equivalents of the geometric notions and methods of classical differential geometry. In this survey we discuss the following two fundamental Discretization Principles: the transformation group principle (smooth geometric objects and their discretizations are invar…
Survey on automating geometry problem solving with large models.
problem Automating geometric problem solving with spatial understanding and logical reasoning.
method Synthesizes GPS advancements through benchmark construction, parsing, and reasoning paradigms.
result Unified analytical paradigm and emerging opportunities identified.
Survey of AI in finance covering models, strategies, and knowledge systems.
problem Challenges in applying AI to financial markets, especially in high-frequency trading.
method Systematic analysis of financial AI across predictive models, decision frameworks, and knowledge augmentation systems.
result Critical trade-offs and gaps between theoretical advances and practical implementation in financial AI.
Survey examines agentic AI in finance, highlighting its autonomy and challenges.
problem Autonomous AI systems in finance and their implications.
method Systematic review of research, technical architectures, market applications, and governance frameworks.
result Agentic AI offers enhanced market efficiency but introduces new risks.
This paper reviews deep time-series forecasting focusing on autocorrelation modeling.
problem Modeling autocorrelation in history and label sequences for time-series forecasting.
method Proposes a novel taxonomy for model architectures and learning objectives.
result Provides a comprehensive review and analysis of deep time-series forecasting.
Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the fiel…
Survey on ML for wireless network optimization across PHY, MAC, and network layers.
problem Improving wireless network performance using machine learning.
method Comprehensive review of ML-based techniques for wireless network optimization.
result Machine learning can significantly enhance wireless network QoS and QoE across all layers.
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prosp…
Survey tackles challenges in neural architecture design.
problem Challenges in designing optimal neural architectures.
method New classification perspective based on early NAS algorithms' characteristics, problems, and solutions.
result Comprehensive analysis and comparison of NAS works.
Survey explores how transfer learning improves deep reinforcement learning.
problem Challenges in reinforcement learning efficiency and effectiveness.
method Categorizes and analyzes transfer learning approaches.
result Transfer learning enhances reinforcement learning performance.
Survey of deep learning methods for graph similarity.
problem Learning a similarity metric among graphs.
method Deep learning models mapping graphs to a target space.
result Systematic taxonomy of existing methods and applications.
Survey classifies Clustered Federated Learning into three types of approaches.
problem Non-independent and identically distributed (non-IID) data in Federated Learning.
method Systematic review of CFL literature, principled taxonomy.
result Core CFL and Metadata-based approaches have distinct focuses.
Survey on using knowledge graphs for better recommender systems.
problem Data sparsity and cold start issues in recommender systems.
method Utilizes knowledge graphs to improve recommendation accuracy and provide explanations.
result Advantages of knowledge graph-based recommender systems.
Survey on understanding neural networks for medical applications.
problem Black-box nature of deep neural networks hinders their use in critical applications.
method Comprehensive review of interpretability studies in neural networks.
result Interpretability research is crucial for the acceptance of neural networks in medical diagnosis.
Survey on negative transfer in machine learning.
problem Negative transfer in transfer learning reduces target domain performance.
method Systematic review of 50+ approaches to mitigate negative transfer.
result Lack of systematic survey on negative transfer.
The present volume is the written version of the series of lectures the author delivered at the Catholic University of Leuven, Belgium during the period of June-July, 1990. The main purpose of these talks is to present some of author's work and also his joint works with Professor T. Nagano and Professor Y. Tazawa of Ja…
Survey of yield farming protocols in DeFi.
problem Understanding and evaluating yield farming mechanisms in DeFi.
method Analyzed smart contracts, performed simulations, reviewed literature.
result Characterized major yield aggregators and identified risks.
The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning…
Transformers improve time series modeling by capturing long-range dependencies.
problem Capturing long-range dependencies in time series data.
method Summarized and reviewed adaptations of Transformers for time series analysis.
result Transformers enhance time series forecasting, anomaly detection, and classification.
Survey on computational models in dynamical systems, including new universality concepts.
problem Understanding the relationship between computational models and dynamical systems.
method Review of recent works on Turing universality, Topological Kleene Field Theories, and dynamical bordisms.
result Introduction of new perspectives on computability through dynamical systems.
Survey of reinforcement learning for sustainable energy challenges.
problem Sequential decision-making challenges in sustainable energy.
method Reinforcement learning applied to energy production, storage, transmission, and consumption.
result Survey identifies various reinforcement learning themes and potential future directions.
Survey of GANs challenges and solutions for better model design and optimization.
problem Challenges in training GANs, including mode collapse, non-convergence, and instability.
method Comprehensive survey of GANs design and optimization solutions, proposing a new taxonomy.
result Presentation of promising research directions in GANs.
Survey on reproducibility and distortion issues in text clustering and topic modeling.
problem Reproducibility and misleading cluster geometry in unsupervised learning for text categorization.
method Systematic literature review of text clustering and topic modeling from 2011-2022.
result Outliers and initialization issues are significant factors in text clustering and topic modeling.
This paper surveys large-scale machine learning methods for efficient data analysis.
problem Efficiently processing large-scale data with machine learning models.
method Divided into three categories: model simplification, optimization approximation, and computation parallelism.
result Blueprint for future developments in large-scale machine learning.
Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Although lots of real-world problems can be viewed as STSF and many research wo…
Survey explores methods to adapt deep learning models across multiple labeled domains.
problem Difficulty in obtaining labeled data for deep learning models.
method Multi-source domain adaptation (MDA) to transfer knowledge from labeled to unlabeled or sparsely labeled target domains.
result MDA methods improve performance by minimizing domain shift.
This paper surveys various data balancing methods for imbalanced datasets.
problem Imbalanced datasets bias predictions and degrade classifier performance.
method Extensive review of oversampling, undersampling, adaptive, generative, combination, and ensemble methods.
result No single method universally outperforms others; selection depends on dataset characteristics.
Survey compares methods for generating artificial outliers.
problem Difficulty in detecting genuine outliers.
method Generates artificial outliers to approximate genuine ones.
result Variability in quality of generation approaches.
Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to a…
Survey on deep learning robust training methods for noisy labels.
problem Dealing with noisy labels in deep learning models.
method Comprehensive review of 62 robust training methods categorized by their approach.
result Analysis of noise rate estimation and evaluation methodologies.
Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and …
Survey of RL methods for control systems with time delays.
problem Time delays in cyber-physical systems degrade RL performance and stability.
method Categorizes and analyzes five major families of RL methods for time delays.
result Identifies key trade-offs and practical guidelines for selecting RL methods.
This paper surveys gradient-based multi-objective deep learning methods.
problem Balancing multiple conflicting objectives in deep learning models.
method Gradient-based techniques adapted from Multi-Objective Optimization.
result Comprehensive survey of gradient-based multi-objective deep learning algorithms.
This paper surveys UQ methods for deep learning.
problem Overconfident predictions in deep learning models.
method Categorizes UQ methods by uncertainty sources.
result Identifies strengths and limitations of each category.