Learning to Help in Multi-Class Settings
Yu Wu, Yansong Li, Zeyu Dong, Nitya Sathyavageeswaran, Anand D. Sarwate·January 23, 2025
Summary
A hybrid system combining local and server-side models optimizes resource use in multi-class classification, addressing constraints. The Learning to Help (L2H) model, extended for multi-class scenarios, trains a server model with a fixed local model, aiming for efficient resource utilization and cost minimization. A differentiable, convex stage-switching surrogate loss function is derived, consistent with the Bayes rule for the L2H model. Experiments validate its effectiveness in resource-limited environments. A conference paper at ICLR 2025 introduces a rejector for selecting local or server models in ML systems, extending the "Learning to Help" framework to practical scenarios. It proposes a generalized 0-1 loss for performance measurement, a convex surrogate loss, and algorithms for training multi-class predictors under these conditions. The paper demonstrates that incorporating a server model with a rejector enhances overall system performance.
Introduction
Background
Overview of multi-class classification challenges
Importance of resource optimization in machine learning systems
Objective
Aim of combining local and server-side models
Goal: efficient resource utilization and cost minimization
Method
Data Collection
Techniques for gathering data for local and server models
Data Preprocessing
Methods for preparing data for model training
Learning to Help (L2H) Model Extension
Overview of the L2H model
Adaptation for multi-class scenarios
Training a server model with a fixed local model
Derivation of Surrogate Loss Function
Explanation of the differentiable, convex stage-switching surrogate loss function
Consistency with the Bayes rule for the L2H model
Experiments
Resource-Limited Environment Validation
Description of experimental setup
Results demonstrating effectiveness of the hybrid system
Conference Paper: "Learning to Help" Framework Extension
Rejection Strategy
Introduction of a rejector for model selection
Integration of a server model with a rejector
Generalized Performance Metrics
Explanation of the generalized 0-1 loss
Convex Surrogate Loss Function
Description and derivation of the convex surrogate loss
Training Algorithms
Overview of algorithms for training multi-class predictors
System Performance Enhancement
Demonstration of improved overall system performance with the proposed framework
Conclusion
Summary of Contributions
Future Work
Implications for Resource-Constrained ML Systems
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does the proposed framework enhance overall system performance according to the user input?
What is the main idea of the user input?
How does the Learning to Help (L2H) model extend for multi-class scenarios?
What is the purpose of the differentiable, convex stage-switching surrogate loss function derived in the user input?