Warm-start or cold-start? A comparison of generalizability in gradient-based hyperparameter tuning

Key Points

• Warm-start tuning improves generalizability compared to cold-start methods—key for algorithm efficiency.
• Performance metrics reveal that warm-start leads to faster convergence in gradient-based tuning methods.
• Observational analysis on various algorithms shows substantial improvements with warm-start methodology.
• This study highlights the need for enhanced strategies in hyperparameter tuning to optimize algorithm performance.