416 citations
- IBM (United States)US5 papers
- Massachusetts Institute of TechnologyUS4 papers
- University of MichiganUS3 papers
- ETH ZurichCH2 papers
- Microsoft Research (United Kingdom)GB2 papers
- Advanced Micro Devices (United States)US1 paper
- Columbia UniversityUS1 paper
- Consejo Superior de Investigaciones CientíficasES1 paper
- Duke UniversityUS1 paper
- Financial Research (Hungary)HU1 paper
- Georgia State UniversityUS1 paper
- IBM Research - AlmadenUS1 paper
17 papers
Safe and Policy-Compliant Multi-Agent Orchestration for Enterprise AI
Vinil Pasupuleti, Shyalendar Reddy Allala, Siva Rama Krishna Varma Bayyavarapu +1
Enterprise AI systems increasingly deploy multiple intelligent agents across mission-critical workflows that must satisfy hard policy constraints, bounded risk exposure, and compre…
Fixing It in Post: A Comparative Study of LLM Post-Training Data Quality and Model Performance
Aladin Djuhera, Swanand Ravindra Kadhe, Syed Zawad +3
Recent work on large language models (LLMs) has increasingly focused on post-training and alignment with datasets curated to enhance instruction following, world knowledge, and spe…
CiMBA: Accelerating Genome Sequencing through On-Device Basecalling via Compute-in-Memory
William Andrew Simon, Irem Boybat, Riselda Kodra +8
As genome sequencing is finding utility in a wide variety of domains beyond the confines of traditional medical settings, its computational pipeline faces two significant challenge…
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Gaoyuan Zhang, Songtao Lu, Yihua Zhang +7
Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against…
Individually Fair Gradient Boosting
Alexander Vargo, Fan Zhang, Mikhail Yurochkin +1
We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in appli…
Statistical inference for individual fairness
Subha Maity, Songkai Xue, Mikhail Yurochkin +1
As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e.g., gende…