2 papers
cs.AI2026
Scaling Laws for Task-Specific LLM Distillation
Lavinia Ghita, Dhruv Desai, Ioana Boier
Large Language Models (LLMs) achieve strong performance across a growing range of domains, yet their scale poses deployment challenges in applications where latency and cost constr…
q-fin.PM2025
Scaling Conditional Autoencoders for Portfolio Optimization via Uncertainty-Aware Factor Selection
Ryan Engel, Yu Chen, Pawel Polak +1
Conditional Autoencoders (CAEs) offer a flexible, interpretable approach for estimating latent asset-pricing factors from firm characteristics. However, existing studies usually li…