A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.
Ant colony optimization for clustering with improved K-means.
This study shows how social insects and machine learning methods share a common mathematical framework.
Math model helps bees decide between winter survival and raising young.
LAAT detects multiple low-density manifolds in noisy data.
Model learns evolving network relationships over time.
A fuzzy expert system selects stocks for BSE using AI techniques.
In this paper we propose DeepSwarm, a novel neural architecture search (NAS) method based on Swarm Intelligence principles. At its core DeepSwarm uses Ant Colony Optimization (ACO) to generate ant population which uses the pheromone information to collectively search for the best neural architecture. Furthermore, by us…
New clustering methods for binary data using combinatorial optimization.
Metaheuristics improve yield curve estimation for Costa Rica.
This paper argues for decolonizing AI alignment by incorporating open-source Hinduism concepts.
This paper studies the interrelation between spot and futures prices in the two major rice markets in prewar Japan from the perspective of market efficiency. Applying a non-Bayesian time-varying model approach to the fundamental equation for spot returns and the futures premium, we detect when efficiency reductions in …
Study of ants' movement rules on a 6D space, revealing distribution structures and singular trajectories.
AgABC improves ABC algorithm by balancing exploration and exploitation.
HSBC grew from colonial China through wars, surviving unethical practices.
Interactive steering improves hierarchical clustering for diverse user needs.
NetDP predicts loan defaults using network data, addressing cold-start issues.
This paper uses decolonial theory to improve AI's ethical development.
In the knowledge that the ex-post performance of Markowitz efficient portfolios is inferior to that implied ex-ante, we make two contributions to the portfolio selection literature. Firstly, we propose a methodology to identify the region of risk-expected return space where ex-post performance matches ex-ante estimates…
Deep learning autoencoder detects bee colony anomalies.
This study analyzes how colonial rice trade in prewar Japan affected its rice market, considering several government interventions in the two rice futures exchanges in Tokyo and Osaka. We explore the interventions in the futures markets using two procedures. First, we measure the joint degree of efficiency in the marke…
Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite a rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by which complex behavior emerges in these systems is still not well und…
The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressive success. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par w…
This paper investigates how realized and option implied volatilities are related to the future quantiles of commodity returns. Whereas realized volatility measures ex-post uncertainty, volatility implied by option prices reveals the market's expectation and is often used as an ex-ante measure of the investor sentiment.…
ANT improves TS diffusion models by automatically determining noise schedules.
For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally finds an Approximate NewTon (ANT) direction, which is sent to the main driver. The main driver, then, …
ANT learns sparse embeddings for large vocabularies efficiently.
Tracking large numbers of densely-arranged, interacting objects is challenging due to occlusions and the resulting complexity of possible trajectory combinations, as well as the sparsity of relevant, labeled datasets. Here we describe a novel technique of collective tracking in the model environment of a 2D honeybee hi…
Adaptive Nucleus Truncation Improves Long-Form Reasoning
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech compani…
Framework selects real estate redevelopment uses by integrating value, risk, complexity, and irreversibility.
New risk-sharing rules induced by capital allocation principles.
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neur…
New method separates model and non-model risks for more practical asset pricing.
The tick value is a crucial component of market design and is often considered the most suitable tool to mitigate the effects of high frequency trading. The goal of this paper is to demonstrate that the approach introduced in Dayri and Rosenbaum (2015) allows for an ex ante assessment of the consequences of a tick valu…
The paper predicts and explains the decay of stock anomaly performance over time.
A novel approach learns goal-conditioned policies for locomotion using batch RL.
Multi-layer optical film has been found to afford important applications in optical communication, optical absorbers, optical filters, etc. Different algorithms of multi-layer optical film design has been developed, as simplex method, colony algorithm, genetic algorithm. These algorithms rapidly promote the design and …
Collaborative filtering, especially latent factor model, has been popularly used in personalized recommendation. Latent factor model aims to learn user and item latent factors from user-item historic behaviors. To apply it into real big data scenarios, efficiency becomes the first concern, including offline model train…
Study uses chatbot to understand users' needs for ML model explanations.
AGFN improves causal discovery by integrating expert feedback and handling latent confounding.
This paper presents an analysis of the study variables such as gdp, employment levels, the level of R & D and technology that will serve as the basis for stochastic modeling of production possibilities frontier in the goodness of fractal dimensions Ex Ante and Ex Post a priori to determine the levels of causality immed…
The paper examines how risk reduction and insurance choices interact under convex premium principles.
Paper compares solutions of Poisson equations on Riemannian manifolds with Robin boundary.
This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an uncon…
We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized …
Researchers visualize all surfaces from tesseract faces.
Climate volatility reduces economic growth, especially in poorer countries.