model selection
The process of choosing the most appropriate model from a set of candidates based on performance metrics, cross-validation, and other criteria. Effective model selection is critical for achieving optimal results in machine learning projects.
- ClinBench: A Standardized Multi-Domain Framework for Evaluating Large Language Models in Clinical Information Extraction
- LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought
- Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
- Lookahead Routing for Large Language Models
- MESS+: Dynamically Learned Inference-Time LLM Routing in Model Zoos with Service Level Guarantees
- Principled Model Routing for Unknown Mixtures of Source Domains
- Probing Equivariance and Symmetry Breaking in Convolutional Networks
- Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start
- SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era.
- The Rashomon Set Has It All: Analyzing Trustworthiness of Trees under Multiplicity
- Transcending Cost-Quality Tradeoff in Agent Serving via Session-Awareness
- Valid Selection among Conformal Sets