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Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a systematic taxonomy of clustering methods that utilize deep…

Machine Learning · Computer Science 2018-09-17 Elie Aljalbout , Vladimir Golkov , Yawar Siddiqui , Maximilian Strobel , Daniel Cremers

Behavioral Finance has become a challenge to the scientific community. Based on the assumption that behavioral aspects of investors may explain some features of the Stock Market, we propose an agent based model to study quantitatively this…

General Finance · Quantitative Finance 2017-11-23 F. M. Stefan , A. P. F. Atman

Agent-based models provide a constructive approach to studying emergent dynamics in life-like systems composed of interacting, adaptive agents. Financial markets serve as a canonical example of such systems, where collective price dynamics…

Computational Finance · Quantitative Finance 2026-04-28 Ryuji Hashimoto , Ryosuke Takata , Masahiro Suzuki , Yuki Tanaka , Kiyoshi Izumi

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of the agent-based models from…

Statistical Finance · Quantitative Finance 2017-03-21 T. T. Chen , B. Zheng , Y. Li , X. F. Jiang

In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet…

General Finance · Quantitative Finance 2015-04-09 Jun-Jie Chen , Lei Tan , Bo Zheng

Coordinating multi-agent systems requires balancing synchronization performance and controller implementation costs. To this end, we classify agents by their intrinsic properties, enabling each group to be controlled by a uniform controller…

Systems and Control · Electrical Eng. & Systems 2025-07-21 Honghao Wu , Kemi Ding , Li Qiu

The proliferation of deep learning techniques led to a wide range of advanced analytics applications in important business areas such as predictive maintenance or product recommendation. However, as the effectiveness of advanced analytics…

Machine Learning · Computer Science 2022-12-07 Peter Kowalczyk , Giacomo Welsch , Frédéric Thiesse

Unsupervised learning is widely recognized as one of the most important challenges facing machine learning nowa- days. However, in spite of hundreds of papers on the topic being published every year, current theoretical understanding and…

Machine Learning · Computer Science 2018-05-24 Shai Ben-David

In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The problem is best described in terms of future buy and sell…

Artificial Intelligence · Computer Science 2013-01-14 Milos Hauskrecht , Eli Upfal

Semi-supervised clustering seeks to augment traditional clustering methods by incorporating side information provided via human expertise in order to increase the semantic meaningfulness of the resulting clusters. However, most current…

Machine Learning · Computer Science 2014-02-17 Caiming Xiong , David Johnson , Jason J. Corso

Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is…

Computation and Language · Computer Science 2020-10-26 Linyi Yang , Eoin M. Kenny , Tin Lok James Ng , Yi Yang , Barry Smyth , Ruihai Dong

Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as features in a vectorized form through various representation…

Machine Learning · Computer Science 2022-06-16 Sheng Zhou , Hongjia Xu , Zhuonan Zheng , Jiawei Chen , Zhao li , Jiajun Bu , Jia Wu , Xin Wang , Wenwu Zhu , Martin Ester

Even though large language models (LLMs) have demonstrated remarkable capability in solving various natural language tasks, the capability of an LLM to follow human instructions is still a concern. Recent works have shown great improvements…

Computation and Language · Computer Science 2024-03-05 Xinbo Wu , Lav R. Varshney

With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial…

Data Structures and Algorithms · Computer Science 2015-12-01 Ka-Chun Wong

Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that…

Machine Learning · Statistics 2018-11-28 Suproteem K. Sarkar , Kojin Oshiba , Daniel Giebisch , Yaron Singer

Finding well-defined clusters in data represents a fundamental challenge for many data-driven applications, and largely depends on good data representation. Drawing on literature regarding representation learning, studies suggest that one…

Machine Learning · Computer Science 2020-11-05 Daniel Lutscher , Ali el Hassouni , Maarten Stol , Mark Hoogendoorn

Learning agent behaviors from observational data has shown to improve our understanding of their decision-making processes, advancing our ability to explain their interactions with the environment and other agents. While multiple learning…

Machine Learning · Computer Science 2023-06-27 Andrea Coletta , Svitlana Vyetrenko , Tucker Balch

Cluster analysis has become one of the most exercised research areas over the past few decades in computer science. As a consequence, numerous clustering algorithms have already been developed to find appropriate partitions of a set of…

Human-Computer Interaction · Computer Science 2016-10-26 Abhisek Dash , Sujoy Chatterjee , Tripti Prasad , Malay Bhattacharyya

Businesses, governmental bodies and NGO's have an ever-increasing amount of data at their disposal from which they try to extract valuable information. Often, this needs to be done not only accurately but also within a short time frame.…

Machine Learning · Computer Science 2021-09-16 Pim Verschuuren , Serena Palazzo , Tom Powell , Steve Sutton , Alfred Pilgrim , Michele Faucci Giannelli

In agricultural management, precise Ground Truth (GT) data is crucial for accurate Machine Learning (ML) based crop classification. Yet, issues like crop mislabeling and incorrect land identification are common. We propose a multi-level GT…

Computer Vision and Pattern Recognition · Computer Science 2025-03-18 Sanayya A , Amoolya Shetty , Abhijeet Sharma , Venkatesh Ravichandran , Masthan Wali Gosuvarapalli , Sarthak Jain , Priyamvada Nanjundiah , Ujjal Kr Dutta , Divya Sharma
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