activity
20242026
collaborators

7 papers

cs.CL2026

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…

cs.LG2026

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…

cs.LG2025

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-…

cs.AI2025

Causal-Copilot: An Autonomous Causal Analysis Agent

Xinyue Wang, Kun Zhou, Wenyi Wu +10

Causal analysis plays a foundational role in scientific discovery and reliable decision-making, yet it remains largely inaccessible to domain experts due to its conceptual and algo…

cs.LG2025

A Lightweight Method to Disrupt Memorized Sequences in LLM

Parjanya Prajakta Prashant, Kaustubh Ponkshe, Babak Salimi

As language models scale, their performance improves dramatically across a wide range of tasks, but so does their tendency to memorize and regurgitate parts of their training data…

cs.LG2024

Differentiable Causal Discovery For Latent Hierarchical Causal Models

Parjanya Prashant, Ignavier Ng, Kun Zhang +1

Discovering causal structures with latent variables from observational data is a fundamental challenge in causal discovery. Existing methods often rely on constraint-based, iterati…

cs.LG2024

Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders

Parjanya Prashant, Seyedeh Baharan Khatami, Bruno Ribeiro +1

We consider the task of out-of-distribution (OOD) generalization, where the distribution shift is due to an unobserved confounder () affecting both the covariates () and the…