1 citations · 1 across the 1 of their papers we have counts for
6 papers
Chance-Constrained DC Optimal Power Flow Using Constraint-Informed Statistical Estimation
Tianyang Yi, D. Adrian Maldonado, Anirudh Subramanyam
Chance-constrained optimization has emerged as a promising framework for managing uncertainties in power systems. This work advances its application to the DC Optimal Power Flow (D…
Scaling Laws Revisited: Modeling the Role of Data Quality in Language Model Pretraining
Anirudh Subramanyam, Yuxin Chen, Robert L. Grossman
Scaling laws for language model training traditionally characterize how performance scales with model size and dataset volume. Prior work has explored architecture variants and dat…
On Solving Chance-Constrained Models with Gaussian Mixture Distribution
Shibshankar Dey, Sanjay Mehrotra, Anirudh Subramanyam
We study linear chance-constrained problems where the coefficients follow a Gaussian mixture distribution. We provide mixed-binary quadratic programs that give inner and outer appr…
LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation
Steven Song, Anirudh Subramanyam, Irene Madejski +1
In the current paradigm of image captioning, deep learning models are trained to generate text from image embeddings of latent features. We challenge the assumption that fine-tunin…
GDC Cohort Copilot: An AI Copilot for Curating Cohorts from the Genomic Data Commons
Steven Song, Anirudh Subramanyam, Zhenyu Zhang +2
The Genomic Data Commons (GDC) provides access to high quality, harmonized cancer genomics data through a unified curation and analysis platform centered around patient cohorts. Wh…
Reduced Sample Complexity in Scenario-Based Control System Design via Constraint Scaling
Jaeseok Choi, Anand Deo, Constantino Lagoa +1
The scenario approach is widely used in robust control system design and chance-constrained optimization, maintaining convexity without requiring assumptions about the probability…