arxiv_ml 92% Match Letter/Research Paper ML researchers,Data scientists,ML engineers,Practitioners deploying models in new domains 19 hours ago

CFL: On the Use of Characteristic Function Loss for Domain Alignment in Machine Learning

ai-safety › robustness

📄 Abstract

Abstract: Machine Learning (ML) models are extensively used in various applications due to their significant advantages over traditional learning methods. However, the developed ML models often underperform when deployed in the real world due to the well-known distribution shift problem. This problem can lead to a catastrophic outcomes when these decision-making systems have to operate in high-risk applications. Many researchers have previously studied this problem in ML, known as distribution shift problem, using statistical techniques (such as Kullback-Leibler, Kolmogorov-Smirnov Test, Wasserstein distance, etc.) to quantify the distribution shift. In this letter, we show that using Characteristic Function (CF) as a frequency domain approach is a powerful alternative for measuring the distribution shift in high-dimensional space and for domain adaptation.

Key Contributions

Demonstrates that using the Characteristic Function (CF) in the frequency domain is a powerful alternative for measuring distribution shift in high-dimensional spaces, offering a new approach for domain adaptation. This method provides a robust way to quantify shifts that can lead to catastrophic outcomes in high-risk applications.

Business Value

Improves the reliability and robustness of ML models deployed in diverse real-world environments, reducing risks associated with distribution shift and enabling safer operation in critical applications.

Paper Metadata

Innovation Type

Algorithmic Innovation

Deployment Feasibility

High. The method provides a quantifiable metric that can be integrated into MLOps pipelines for monitoring and adaptation.

Limitations Addressed

Underperformance of ML models in real-world deployment due to distribution shift, limitations of existing statistical techniques for quantifying shift in high-dimensional spaces.

Technical Tags

domain alignmentdistribution shiftcharacteristic functionfrequency domaindomain adaptationhigh-dimensional spacestatistical techniquesmachine learning

Research Topics

Domain AdaptationDistribution ShiftMachine Learning RobustnessStatistical Learning Theory

Methods & Architectures

Characteristic Function (CF) analysisFrequency Domain Analysis

Applications & Tasks

Machine Learning Deployment Real-world Applications Distribution ShiftDomain AdaptationModel Underperformance in New Domains Domain AlignmentQuantifying Distribution Shift

Related Fields

StatisticsMachine LearningSignal ProcessingInformation Theory

Keywords

distribution shiftdomain adaptationcharacteristic functionfrequency domainmachine learningrobustnesshigh-dimensional datastatistical learningmodel deploymentreal-world AI

Academic Context

#Domain Adaptation#Distribution Shift#Machine Learning Robustness#Statistical Learning Theory

Commercial Potential

Potential Products

Domain shift detection toolsAutomated domain adaptation frameworks

Target Industries

All industries deploying ML modelsFinanceHealthcareAutonomous Systems

Use Case Examples

Ensuring medical diagnosis models work across different hospitalsAdapting autonomous driving models to new weather conditionsMaintaining fraud detection models across evolving financial patterns

Competitive Edge

Offers a novel frequency-domain approach to distribution shift measurement, potentially overcoming limitations of existing time-domain statistical methods in high-dimensional settings.

Market Opportunity

Large and growing market for robust and reliable AI solutions.

Revenue Models

Licensing of detection/adaptation algorithmsconsulting services.

Resource Requirements

Compute Needs

Low to Moderate

Data Requirements

Data from source and target domains.

Deployment Constraints

Requires access to data from both source and target distributions.

Scalability

Scalability depends on the efficiency of CF computation in high dimensions.

Regulatory Considerations

Ensuring fairness and avoiding bias amplification due to domain shift.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years

Patent Potential

Moderate

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