119 citations · 265 across the 13 of their papers we have counts for
14 papers
Agents in the Wild: Where Research Meets Deployment
Grace Hui Yang, Pranav N. Venkit, Hooman Sedghamiz +3
Agentic systems large language model (LLM) based architectures capable of reasoning, planning, acting, and coordinating with tools and other agents are rapidly transitioning from r…
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor +35
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (trai…
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
Hammaad Adam, Ming Ying Yang, Kenrick Cato +6
Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpet…
Downstream Fairness Caveats with Synthetic Healthcare Data
Karan Bhanot, Ioana Baldini, Dennis Wei +2
This paper evaluates synthetically generated healthcare data for biases and investigates the effect of fairness mitigation techniques on utility-fairness. Privacy laws limit access…
Ground-Truth, Whose Truth? -- Examining the Challenges with Annotating Toxic Text Datasets
Kofi Arhin, Ioana Baldini, Dennis Wei +2
The use of machine learning (ML)-based language models (LMs) to monitor content online is on the rise. For toxic text identification, task-specific fine-tuning of these models are…
Your fairness may vary: Pretrained language model fairness in toxic text classification
Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy +2
The popularity of pretrained language models in natural language processing systems calls for a careful evaluation of such models in down-stream tasks, which have a higher potentia…
Biomedical Interpretable Entity Representations
Diego Garcia-Olano, Yasumasa Onoe, Ioana Baldini +3
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretabl…
Automated Meta-Analysis: A Causal Learning Perspective
Lu Cheng, Dmitriy A. Katz-Rogozhnikov, Kush R. Varshney +1
Meta-analysis is a systematic approach for understanding a phenomenon by analyzing the results of many previously published experimental studies. It is central to deriving conclusi…
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran +7
More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe p…
How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?
Yaoli Mao, Dakuo Wang, Michael Muller +4
In recent years there has been an increasing trend in which data scientists and domain experts work together to tackle complex scientific questions. However, such collaborations of…
Teaching machines to understand data science code by semantic enrichment of dataflow graphs
Evan Patterson, Ioana Baldini, Aleksandra Mojsilovic +1
Your computer is continuously executing programs, but does it really understand them? Not in any meaningful sense. That burden falls upon human knowledge workers, who are increasin…
Serverless Computing: Current Trends and Open Problems
Ioana Baldini, Paul Castro, Kerry Chang +8
Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programming models, abstractions,…
I know what you mean: semantic issues in Internet-scale publish/subscribe systems
Ioana Burcea, Milenko Petrovic, Hans-Arno Jacobsen
In recent years, the amount of information on the Internet has increased exponentially developing great interest in selective information dissemination systems. The publish/subscri…
S-ToPSS: Semantic Toronto Publish/Subscribe System
Milenko Petrovic, Ioana Burcea, Hans-Arno Jacobsen
The increase in the amount of data on the Internet has led to the development of a new generation of applications based on selective information dissemination where, data is distri…