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.
Community-based system dynamics improves ML fairness by involving excluded stakeholders.
Blockchain fan tokens boost sports fan engagement by 50%.
In this paper, we have discussed initial findings and results of our experiment to predict sexual and reproductive health vulnerabilities of migrants in a data-constrained environment. Notwithstanding the limited research and data about migrants and migration cities, we propose a solution that simultaneously focuses on…
EXAGREE selects a stakeholder-aligned model to reduce conflicting explanations in machine learning.
NFTs raise concerns like scams, racism, and sexism; centralization vs decentralization debate.
A model for groundwater trading among stakeholders.
Proposes measuring fairness through multiple stakeholder-curated stress tests.
Managers of US National Forests must decide what policy to apply for dealing with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber companies, home owners, and wildlife biologists) have often led to spirited political debates and even violent eco-terrorism. One way to transform these conflicts into…
Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work…
PRIME models cryptocurrency exchange market impact.
Sample-Rank simplifies MO recommendations by sampling and ranking, improving revenue with stable conversion rates.
Calls to arms to build interpretable models express a well-founded discomfort with machine learning. Should a software agent that does not even know what a loan is decide who qualifies for one? Indeed, we ought to be cautious about injecting machine learning (or anything else, for that matter) into applications where t…
Explainable machine learning offers the potential to provide stakeholders with insights into model behavior by using various methods such as feature importance scores, counterfactual explanations, or influential training data. Yet there is little understanding of how organizations use these methods in practice. This st…
Decision support systems (e.g., for ecological conservation) and autonomous systems (e.g., adaptive controllers in smart cities) start to be deployed in real applications. Although their operations often impact many users or stakeholders, no fairness consideration is generally taken into account in their design, which …
Proposes a method to infer the distributional impacts of predictive models on stakeholders.
Models assess how USDA orange production forecasts impact FCOJ market decisions.
Interpretable AI model boosts investment confidence and profitability.
FinTech framework clusters innovations for financial services.
This paper introduces STAP to measure DEX efficiency and shows better routing algorithms increase DEX performance and stakeholder benefits.
Optimizes capital structure for life insurance companies with surplus participation.
The paper uncovers two key laws of market impact influenced by volume and participation rate.
City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the bigg…
Study uses VC correlation to uncover directional financial relationships.
Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI d…
Local adaptation improves federated learning models.
Unified analysis of FL with arbitrary client participation.
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label informa…
Improved NTL detection using human-in-the-loop approach with explainability.
FedAMD framework improves federated learning with partial client participation.
Study reveals dynamic causal relationships between Ethereum transaction fees and economic subsystems.
Paper tackles unknown participation in FL, proposing FedAU for better performance.
This paper rates robustness of multi-modal time-series forecasting models.
The purpose of this article is to introduce, analyze and compare two performance participation methods based on a portfolio consisting of two risky assets: Option-Based Performance Participation (OBPP) and Constant Proportion Performance Participation (CPPP). By generalizing the provided guarantee to a participation in…
Extended model ensures long-term survival of traders in limited stock market participation.
Data poisoning attacks can severely degrade FL models, especially targeting specific classes.
In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand …
Proves existence of equilibrium in limited participation economy.
In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor pe…
Flexible device participation improves federated learning convergence.
Neuroimaging studies produce gigabytes of spatio-temporal data for a small number of participants and stimuli. Rarely do researchers attempt to model and examine how individual participants vary from each other -- a question that should be addressable even in small samples given the right statistical tools. We propose …
Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content recommendation and automation. The technical challenges for tackling these problems ar…
Federated learning leaks participant dataset quality even with secure aggregation.
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developer…
A new federated learning framework ensures fairness and robustness.
Artemis framework improves distributed learning with bidirectional compression and partial participation.
Requirements elicitation requires extensive knowledge and deep understanding of the problem domain where the final system will be situated. However, in many software development projects, analysts are required to elicit the requirements from an unfamiliar domain, which often causes communication barriers between analys…
Federated learning enables the creation of a powerful centralized model without compromising data privacy of multiple participants. While successful, it does not incorporate the case where each participant independently designs its own model. Due to intellectual property concerns and heterogeneous nature of tasks and d…
ADRL improves participant selection in MCS systems.