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Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processing tasks. However, their effectiveness is highly dependent on the phrasing of the task prompt, leading to research on automatic prompt…

Computation and Language · Computer Science 2024-02-06 Moxin Li , Wenjie Wang , Fuli Feng , Yixin Cao , Jizhi Zhang , Tat-Seng Chua

Planning and learning in Partially Observable MDPs (POMDPs) are among the most challenging tasks in both the AI and Operation Research communities. Although solutions to these problems are intractable in general, there might be special…

Artificial Intelligence · Computer Science 2012-07-09 Eyal Even-Dar , Sham M. Kakade , Yishay Mansour

Accurate transfer of information across multiple sectors to enhance model estimation is both significant and challenging in multi-sector portfolio optimization involving a large number of assets in different classes. Within the framework of…

Methodology · Statistics 2026-04-23 Qingliang Fan , Ruike Wu , Yanrong Yang

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing…

Astrophysics · Physics 2009-11-11 D. J. Rohde , M. R. Gallagher , M. J. Drinkwater , K. A. Pimbblet

Recommender systems play an essential role in online services by providing personalized item lists to support users' decision-making processes. While collaborative filtering methods can achieve high accuracy, it is crucial to consider not…

Optimization and Control · Mathematics 2026-03-24 Tomoya Yanagi , Shunnosuke Ikeda , Ken Kobayashi , Yuichi Takano

Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbalanced issue can be further exacerbated, making it…

Machine Learning · Computer Science 2023-08-30 Kwei-Herng Lai , Daochen Zha , Huiyuan Chen , Mangesh Bendre , Yuzhong Chen , Mahashweta Das , Hao Yang , Xia Hu

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing…

Artificial Intelligence · Computer Science 2020-07-13 Jinho Lee , Raehyun Kim , Seok-Won Yi , Jaewoo Kang

Optimization underpins decision-making in domains from healthcare to logistics, yet for many practitioners it remains a "magical box": powerful but opaque, difficult to use, and reliant on specialized expertise. While prior work has…

Human-Computer Interaction · Computer Science 2025-09-23 Connor Lawless , Jakob Schoeffer , Madeleine Udell

Managing investment portfolios is an old and well know problem in multiple fields including financial mathematics and financial engineering as well as econometrics and econophysics. Multiple different concepts and theories were used so far…

Portfolio Management · Quantitative Finance 2020-01-08 Jarosław Gruszka , Janusz Szwabiński

Mathematical programming -- the task of expressing operations and decision-making problems in precise mathematical language -- is fundamental across domains, yet remains a skill-intensive process requiring operations research expertise.…

Motion planning for autonomous robots in dynamic environments poses numerous challenges due to uncertainties in the robot's dynamics and interaction with other agents. Sampling-based MPC approaches, such as Model Predictive Path Integral…

Robotics · Computer Science 2024-05-07 Elia Trevisan , Javier Alonso-Mora

Data is a critical asset for training large language models (LLMs), alongside compute resources and skilled workers. While some training data is publicly available, substantial investment is required to generate proprietary datasets, such…

Machine Learning · Computer Science 2026-01-27 Mélissa Tamine , Otmane Sakhi , Benjamin Heymann

Managing stock efficiently remains a core issue in modern logistics, where companies must reconcile cost efficiency with dependable service despite unpredictable market conditions. Conventional models often overlook the direct connection…

Optimization and Control · Mathematics 2026-04-14 Tianxiao Sun , Noah Schwarzkopf

In this paper, we address a variant of the marketing mix optimization (MMO) problem which is commonly encountered in many industries, e.g., retail and consumer packaged goods (CPG) industries. This problem requires the spend for each…

Optimization and Control · Mathematics 2021-01-12 Hsin-Chan Huang , Jiefeng Xu , Alvin Lim

Portfolio optimization is a ubiquitous problem in financial mathematics that relies on accurate estimates of covariance matrices for asset returns. However, estimates of pairwise covariance could be better and calculating time-sensitive…

Portfolio Management · Quantitative Finance 2024-11-12 James S. Cummins , Natalia G. Berloff

Operations is a key challenge in the domain of machine learning pipeline deployments involving monitoring and management of real-time prediction quality. Typically, metrics like accuracy, RMSE etc., are used to track the performance of…

This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector…

Computational Engineering, Finance, and Science · Computer Science 2025-04-11 Ryan Quek Wei Heng , Edoardo Vittori , Keane Ong , Rui Mao , Erik Cambria , Gianmarco Mengaldo

This paper investigates how Large Language Models (LLMs) from leading providers (OpenAI, Google, Anthropic, DeepSeek, and xAI) can be applied to quantitative sector-based portfolio construction. We use LLMs to identify investable universes…

Portfolio Management · Quantitative Finance 2026-01-01 Alina Voronina , Oleksandr Romanko , Ruiwen Cao , Roy H. Kwon , Rafael Mendoza-Arriaga

Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions. These components are typically approached separately: a machine learning model is…

Machine Learning · Computer Science 2018-11-22 Bryan Wilder , Bistra Dilkina , Milind Tambe

The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These features can be used as input for ML models that are trained to…

Machine Learning · Computer Science 2023-06-12 Gjorgjina Cenikj , Gašper Petelin , Carola Doerr , Peter Korošec , Tome Eftimov