44 citations · 63 across the 14 of their papers we have counts for
4 papers · 1 filter
Can You Delete a Year of Market Data? Machine Unlearning Against Exact Retraining Oracles
Junyi Ye
When a data license expires, deleting stored records does not remove influence encoded in a trained forecaster. Machine unlearning seeks to remove this influence without retraining…
Damage Predicts Recovery: When Calibration Data Matters in Compressing Financial LLMs
Junyi Ye, Mengjia Yu, Debapriya Hazra +1
Post-training quantization and pruning rely on a small calibration corpus. Whether specialized domains such as finance require domain-matched calibration data remains unsettled. We…
Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting
Junyi Ye, Ivy Gateri Wanjiku
Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memory and computational cost. Po…
Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting
Junyi Ye, Gargi Vijay Borde
Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should ent…