4 citations · 4 across the 5 of their papers we have counts for
8 papers
Residual Skill Optimization for Text-to-SQL Ensembles
Jiongli Zhu, Haoquan Guan, Parjanya Prajakta Prashant +8
Text-to-SQL ensembles improve over single-candidate generation by drawing multiple SQL candidates and selecting one, but their effectiveness is bounded by Pass@K, the probability t…
Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates
Parjanya Prajakta Prashant, Jiongli Zhu, Aldan Creo +1
Fine-tuning large language models on new data improves task performance but degrades capabilities learned during pretraining, a phenomenon known as catastrophic forgetting. Existin…
KAIROS: Scalable Model-Agnostic Data Valuation
Jiongli Zhu, Parjanya Prajakta Prashant, Alex Cloninger +1
Training data increasingly shapes not only model accuracy but also regulatory compliance and market valuation of AI assets. Yet existing valuation methods remain inadequate: model-…
Stress-Testing ML Pipelines with Adversarial Data Corruption
Jiongli Zhu, Geyang Xu, Felipe Lorenzi +2
Structured data-quality issues, such as missing values correlated with demographics, culturally biased labels, or systemic selection biases, routinely degrade the reliability of ma…
MINT: Multi-Vector Search Index Tuning
Jiongli Zhu, Yue Wang, Bailu Ding +3
Vector search plays a crucial role in many real-world applications. In addition to single-vector search, multi-vector search becomes important for multi-modal and multi-feature sce…
Learning from Uncertain Data: From Possible Worlds to Possible Models
Jiongli Zhu, Su Feng, Boris Glavic +1
We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive…