3 papers
cs.DC2026
Optimizing High-Throughput Distributed Data Pipelines for Reproducible Deep Learning at Scale
Kashish Mittal, Di Yu, Roozbeh Ketabi +3
Training massive-scale deep learning models on datasets spanning tens of terabytes presents critical challenges in hardware utilization and training reproducibility. In this paper,…
cs.CR2024
TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs
Tanmay Rajore, Nishanth Chandran, Sunayana Sitaram +4
Benchmarking is the de-facto standard for evaluating LLMs, due to its speed, replicability and low cost. However, recent work has pointed out that the majority of the open source b…
cs.DS2023
Group Testing for Accurate and Efficient Range-Based Near Neighbor Search for Plagiarism Detection
Harsh Shah, Kashish Mittal, Ajit Rajwade
This work presents an adaptive group testing framework for the range-based high dimensional near neighbor search problem. Our method efficiently marks each item in a database as ne…